Decoupling internal and external tasks in a database environment

ABSTRACT

Systems, methods, and devices for retrying a query. A method includes receiving, by a first database query manager, a query directed to database data from a client account. The method includes assigning an original execution of the query to one or more execution nodes of an execution platform. The method includes determining the original execution of the query was unsuccessful. The method includes transferring the query to a second database query manager configured to manage internal tasks for improving operation of a database platform that are not received from client accounts. The method includes assigning, by the second database query manager, a retry execution of the query to one or more execution nodes of an execution platform.

TECHNICAL FIELD

The present disclosure relates to databases and more particularlyrelates to automated query retry in database systems.

BACKGROUND

Databases are an organized collection of data that enable data to beeasily accessed, manipulated, and updated. Databases serve as a methodof storing, managing, and retrieving information in an efficient manner.Traditional database management requires companies to provisioninfrastructure and resources to manage the database in a data center.Management of a traditional database can be very costly and requiresoversight by multiple persons having a wide range of technical skillsets.

Databases are widely used for data storage and access in computingapplications. A goal of database storage is to provide enormous sums ofinformation in an organized manner so that it can be accessed, managed,and updated. In a database, data may be organized into rows, columns,and tables. Different database storage systems may be used for storingdifferent types of content, such as bibliographic, full text, numeric,and/or image content. Further, in computing, different database systemsmay be classified according to the organization approach of thedatabase. There are many different types of databases, includingrelational databases, distributed databases, cloud databases,object-oriented and others.

Traditional relational database management systems (RDMS) requireextensive computing and storage resources and have limited scalability.Large sums of data may be stored across multiple computing devices. Aserver may manage the data such that it is accessible to customers withon-premises operations. For an entity that wishes to have an in-housedatabase server, the entity must expend significant resources on acapital investment in hardware and infrastructure for the database,along with significant physical space for storing the databaseinfrastructure. Further, the database may be highly susceptible to dataloss during a power outage or other disaster situations. Suchtraditional database systems have significant drawbacks that may bealleviated by a cloud-based database system.

A cloud database system may be deployed and delivered through a cloudplatform that allows organizations and end users to store, manage, andretrieve data from the cloud. Some cloud database systems include atraditional database architecture that is implemented through theinstallation of database software on top of a computing cloud. Thedatabase may be accessed through a Web browser or an applicationprogramming interface (API) for application and service integration.Some cloud database systems are operated by a vendor that directlymanages backend processes of database installation, deployment, andresource assignment tasks on behalf of a client. The client may havemultiple end users that access the database by way of a Web browserand/or API. Cloud databases may provide significant benefits to someclients by mitigating the risk of losing database data and allowing thedata to be accessed by multiple users across multiple geographicregions.

Databases are used by various entities and companies for storinginformation that may need to be accessed or analyzed. In an example, aretail company may store a listing of all sales transactions in adatabase. The database may include information about when a transactionoccurred, where it occurred, a total cost of the transaction, anidentifier and/or description of all items that were purchased in thetransaction, and so forth. The same retail company may also store, forexample, employee information in that same database that might includeemployee names, employee contact information, employee work history,employee pay rate, and so forth. Depending on the needs of this retailcompany, the employee information and transactional information may bestored in different tables of the same database. The retail company mayhave a need to “query” its database when it wants to learn informationthat is stored in the database. This retail company may want to finddata about, for example, the names of all employees working at a certainstore, all employees working on a certain date, all transactions for acertain product made during a certain time frame, and so forth.

When the retail store wants to query its database to extract certainorganized information from the database, a query statement is executedagainst the database data. The query returns certain data according toone or more query predicates that indicate what information should bereturned by the query. The query extracts specific data from thedatabase and formats that data into a readable form. The query may bewritten in a language that is understood by the database, such asStructured Query Language (“SQL”), so the database systems can determinewhat data should be located and how it should be returned. The query mayrequest any pertinent information that is stored within the database. Ifthe appropriate data can be found to respond to the query, the databasehas the potential to reveal complex trends and activities. This powercan only be harnessed through the use of a successfully executed query.

In some instances, the execution of a query fails. Query execution mayfail for a number of different reasons, including an intermittent faultor a software regression. An intermittent fault may be caused by ahardware failure, a power outage, a fault electrical connection, achange in temperature, vibration, and others. Intermittent faults arevery difficult to predict and identify. A software regression may becaused by a bug or error in software code. Software regressions cancause continued issues with query execution and should therefore beidentified and repaired. In some instances, it is desirable to retryfailed queries so that a valid query response can be returned to aclient.

In light of the foregoing, disclosed herein are systems, methods, anddevices for automated query retry in a database system. The systems,methods, and devices disclosed herein provide means for querying data,determining how and where queries should be retried, and analyzing queryretries.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive implementations of the presentdisclosure are described with reference to the following figures,wherein like reference numerals refer to like or similar partsthroughout the various views unless otherwise specified. Advantages ofthe present disclosure will become better understood with regard to thefollowing description and accompanying drawings where:

FIG. 1 is a block diagram illustrating an example process flow forscheduling tasks on a database and decoupling internal and externaltasks in a database platform;

FIG. 2 is a block diagram illustrating a data processing platform;

FIG. 3 is a block diagram illustrating a compute service manager;

FIG. 4 is a block diagram illustrating an execution platform;

FIG. 5 is a block diagram illustrating an example operating environment;

FIG. 6 is a schematic diagram of a process flow for retrying a failedquery;

FIG. 7 is a schematic diagram of a process flow for a query retry run;

FIG. 8 is a block diagram illustrating a resource manager;

FIG. 9 is a schematic flow chart diagram of a method for retrying afailed query;

FIG. 10 is a schematic flow chart diagram of a method for determiningwhether a regression or intermittent fault caused a query to fail;

FIG. 11 is a schematic flow chart diagram of a method for retrying afailed query;

FIG. 12 is a schematic flow chart diagram of a method for generating andfiltering a transaction log for query attempts in a database platform;

FIG. 13 is a schematic flow chart diagram of a method for retrying afailed query;

FIG. 14 is a schematic flow chart diagram of a method for retrying afailed query; and

FIG. 15 is a schematic block diagram of an example computing device.

DETAILED DESCRIPTION

Disclosed herein are systems, methods, and devices for automated queryretry in a database platform. The systems, methods, and devices of thedisclosure can be implemented to automatically retry a failed query andidentify system errors based on the query retry attempts. Embodiments ofthe disclosure can be implemented to identify software regressions in adatabase platform. Additionally, embodiments of the disclosure can beimplemented to distinguish software regressions from intermittentfaults.

In an embodiment of the disclosure, a method may be implemented by aresource manager and/or compute service manager of a database platform.The method includes receiving a query directed to database data andassigning execution of the query to one or more execution nodes of anexecution platform. The one or more execution nodes may be configured toexecute the query on a first software version of the database platform.The method includes determining that execution of the query wasunsuccessful. The method includes assigning a first retry execution ofthe query on the first software version of the database platform andassigning a second retry execution of the query on a second softwareversion of the database platform. The first and second retry executionsof the query may be analyzed to determine whether the original, failedexecution of the query failed due to a software regression or anintermittent fault. In an embodiment, the query is successful at mostone time such that additional retry attempts are scheduled only afterprevious retry attempts have failed. In such an embodiment, if a firstretry attempt is successful, then no additional retry attempts will bescheduled.

Databases are widely used for data storage and data access in computingapplications. Databases may include one or more tables that include orreference data that can be read, modified, or deleted using queries.However, for some modern data warehouse systems, executing a query canbe exceptionally time and resource intensive because modern datawarehouse systems often include tables storing petabytes of data.Querying very large databases and/or tables might require scanning largeamounts of data. Reducing the amount of data scanned for databasequeries is one of the main challenges of data organization andprocessing. When processing a query against a very large sum of data, itcan be important to use materialized views to reduce the amount of timeand processing resources required to execute the query. The systems,methods, and devices of the disclosure provide means for automaticallyretrying failed queries and performing analysis on query retry attempts.

In the following description, reference is made to the accompanyingdrawings that form a part thereof, and in which is shown by way ofillustration specific exemplary embodiments in which the disclosure maybe practiced. These embodiments are described in sufficient detail toenable those skilled in the art to practice the concepts disclosedherein, and it is to be understood that modifications to the variousdisclosed embodiments may be made, and other embodiments may beutilized, without departing from the scope of the present disclosure.The following detailed description is, therefore, not to be taken in alimiting sense.

Reference throughout this specification to “one embodiment,” “anembodiment,” “one example” or “an example” means that a particularfeature, structure or characteristic described in connection with theembodiment or example is included in at least one embodiment of thepresent disclosure. Thus, the appearances of the phrases “in oneembodiment,” “in an embodiment,” “one example” or “an example” invarious places throughout this specification are not necessarily allreferring to the same embodiment or example. In addition, it should beappreciated that the figures provided herewith are for explanationpurposes to persons ordinarily skilled in the art and that the drawingsare not necessarily drawn to scale.

Embodiments in accordance with the present disclosure may be embodied asan apparatus, method or computer program product. Accordingly, thepresent disclosure may take the form of an entirely hardware-comprisedembodiment, an entirely software-comprised embodiment (includingfirmware, resident software, micro-code, etc.) or an embodimentcombining software and hardware aspects that may all generally bereferred to herein as a “circuit,” “module” or “system.” Furthermore,embodiments of the present disclosure may take the form of a computerprogram product embodied in any tangible medium of expression havingcomputer-usable program code embodied in the medium.

Any combination of one or more computer-usable or computer-readablemedia may be utilized. For example, a computer-readable medium mayinclude one or more of a portable computer diskette, a hard disk, arandom-access memory (RAM) device, a read-only memory (ROM) device, anerasable programmable read-only memory (EPROM or Flash memory) device, aportable compact disc read-only memory (CDROM), an optical storagedevice, and a magnetic storage device. Computer program code forcarrying out operations of the present disclosure may be written in anycombination of one or more programming languages. Such code may becompiled from source code to computer-readable assembly language ormachine code suitable for the device or computer on which the code willbe executed.

Embodiments may also be implemented in cloud computing environments. Inthis description and the following claims, “cloud computing” may bedefined as a model for enabling ubiquitous, convenient, on-demandnetwork access to a shared pool of configurable computing resources(e.g., networks, servers, storage, applications, and services) that canbe rapidly provisioned via virtualization and released with minimalmanagement effort or service provider interaction and then scaledaccordingly. A cloud model can be composed of various characteristics(e.g., on-demand self-service, broad network access, resource pooling,rapid elasticity, and measured service), service models (e.g., Softwareas a Service (“SaaS”), Platform as a Service (“PaaS”), andInfrastructure as a Service (“IaaS”)), and deployment models (e.g.,private cloud, community cloud, public cloud, and hybrid cloud).

The flow diagrams and block diagrams in the attached figures illustratethe architecture, functionality, and operation of possibleimplementations of systems, methods, and computer program productsaccording to various embodiments of the present disclosure. In thisregard, each block in the flow diagrams or block diagrams may representa module, segment, or portion of code, which comprises one or moreexecutable instructions for implementing the specified logicalfunction(s). It will also be noted that each block of the block diagramsand/or flow diagrams, and combinations of blocks in the block diagramsand/or flow diagrams, may be implemented by special purposehardware-based systems that perform the specified functions or acts, orcombinations of special purpose hardware and computer instructions.These computer program instructions may also be stored in acomputer-readable medium that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instruction meanswhich implement the function/act specified in the flow diagram and/orblock diagram block or blocks.

The systems and methods described herein provide a flexible and scalabledata warehouse using a new data processing platform. In someembodiments, the described systems and methods leverage a cloudinfrastructure that supports cloud-based storage resources, computingresources, and the like. Example cloud-based storage resources offersignificant storage capacity available on-demand at a low cost. Further,these cloud-based storage resources may be fault-tolerant and highlyscalable, which can be costly to achieve in private data storagesystems. Example cloud-based computing resources are available on-demandand may be priced based on actual usage levels of the resources.Typically, the cloud infrastructure is dynamically deployed,reconfigured, and decommissioned in a rapid manner.

In the described systems and methods, a data storage system may utilizea SQL (Structured Query Language)-based relational database. However,these systems and methods are applicable to any type of database, andany type of data storage and retrieval platform, using any data storagearchitecture and using any language to store and retrieve data withinthe data storage and retrieval platform. The systems and methodsdescribed herein further provide a multi-tenant system that supportsisolation of computing resources and data between differentcustomers/clients and between different users within the samecustomer/client.

As used herein, the terms “comprising,” “including,” “containing,”“characterized by,” and grammatical equivalents thereof are inclusive oropen-ended terms that do not exclude additional, unrecited elements ormethod steps.

As used herein, a database table is a collection of records (rows). Eachrecord contains a collection of values of table attributes (columns).Database tables are typically physically stored in multiple smaller(varying size or fixed size) storage units, e.g. files or blocks.

As used herein, a micro-partition is an immutable storage device in adatabase table that cannot be updated in-place and must be regeneratedwhen the data stored therein is modified.

Some embodiments of the disclosure may refer to a “micro-partition” asstoring a portion of the data in a database table. The micro-partitionas discussed herein may be considered a batch unit where eachmicro-partition has contiguous units of storage. By way of example, eachmicro-partition may contain between 50 MB and 500 MB of uncompresseddata (note that the actual size in storage may be smaller because datamay be stored compressed). Groups of rows in tables may be mapped intoindividual micro-partitions organized in a columnar fashion. This sizeand structure allow for extremely granular selection of themicro-partitions to be scanned, which can be comprised of millions, oreven hundreds of millions, of micro-partitions. This granular selectionprocess may be referred to herein as “pruning” based on metadata.Pruning involves using metadata to determine which portions of a table,including which micro-partitions or micro-partition groupings in thetable, are not pertinent to a query, and then avoiding thosenon-pertinent micro-partitions when responding to the query and scanningonly the pertinent micro-partitions to respond to the query. Metadatamay be automatically gathered about all rows stored in amicro-partition, including: the range of values for each of the columnsin the micro-partition; the number of distinct values; and/or additionalproperties used for both optimization and efficient query processing. Inone embodiment, micro-partitioning may be automatically performed on alltables. For example, tables may be transparently partitioned using theordering that occurs when the data is inserted/loaded. However, itshould be appreciated that this disclosure of the micro-partition isexemplary only and should be considered non-limiting. It should beappreciated that the micro-partition may include other database storagedevices without departing from the scope of the disclosure.

A detailed description of systems and methods consistent withembodiments of the present disclosure is provided below. While severalembodiments are described, it should be understood that this disclosureis not limited to any one embodiment, but instead encompasses numerousalternatives, modifications, and equivalents. In addition, whilenumerous specific details are set forth in the following description inorder to provide a thorough understanding of the embodiments disclosedherein, some embodiments may be practiced without some or all of thesedetails. Moreover, for the purpose of clarity, certain technicalmaterial that is known in the related art has not been described indetail in order to avoid unnecessarily obscuring the disclosure.

Referring now to the figures, FIG. 1 is a block diagram of an exampleembodiment of a process flow 100 for managing and executing query retryjobs on a database system and decoupling external and internal tasks ina database platform. In an embodiment, the process flow 100 is carriedout by a compute service manager 102 that is configured to manage andassign query retry tasks and a resource manager 802 that is configuredto manage and assign the execution of queries received from clientaccounts. In the process flow 100, a resource manager 802 receives aquery from a client account at 124. The resource manager 802 referencesmetadata to identify one or more files that are responsive to the query.The resource manager 802 assigns processing of the one or more files toone or more execution nodes of an execution platform 116 at 126. Theresource manager determines that the original execution of the queryfailed at 128. If the original execution of the query failed due to aninternal error, rather than a non-internal “user error” based on auser's query text or data, then the resource manager 802 transfer thequery to a compute service manager 102 at 130. A non-internal user errormay include an error in the Structured Query Language (SQL) text of thequery, an error based on the actual data being processed. In the processflow 100, the compute service manager 102 receives an indication at 106of a failed query 104. In an embodiment, the compute service manager 102receives this indication by receiving a query retry job in its queue. Inan embodiment, the resource manager 802 transfer the query by placingthe query in the queue of the compute service manager 102. Theindication of the failed query 104 may be a positive/negative indicationthat only indicates the query failed and does not provide additionalinformation about why or when the query failed. The compute servicemanager 102 determines at 108 tasks to be performed to retry the queryand assigns those tasks at 110 to one or more execution nodes of one ormore execution platforms 116. The compute service manager 102 analyzesthe query retry attempt(s) at 112.

The compute service manager 102 shown in FIGS. 1-3 is a representationof a compute service instance. The compute service manager 102 may beone of multiple compute service instances serving a multiple tenantcloud-based database platform. In an embodiment, one or more computeservice managers 102 are assigned to manage all “internal” tasks for asingle client account. The compute service manager 102 may be assignedto manage internal tasks for one or more client accounts. One clientaccount may have multiple compute service managers 102 assigned thereto.The multiple compute service instances are configured to collectivelymanage the execution of internal database tasks that are not receiveddirectly from client accounts. Such internal tasks include, for example,retrying a failed query, refreshing a materialized view, refreshing atable aggregation, clustering a table, and so forth.

The compute service manager 102 may work in connection with one or moreresource managers 802. In an embodiment, there is a resource manager 802for each compute service manager across the database platform. In anembodiment, the number of resource managers 802 and the number ofcompute service managers 102 is unequal. The resource managers 802 aresimilar to the compute service managers 102 with the exception thatresource managers 802 are configured to manage the execution of“external” database tasks that are received from a client account. Suchexternal tasks may include, for example, a query request received from aclient, a request to generate a materialized view, a request tocalculate an aggregation, and so forth. The resource managers mayinclude each of the components associated with the compute servicemanager 102 as illustrated in FIG. 3 and may include additionalcomponents as needed. The compute service managers 102 and the resourcemanagers 802 may work together and may transfer work between oneanother. For example, a query request may originate from a clientaccount and be received by the resource manager 802. If execution of thequery fails, the query retry attempts may be transferred from theresource manager 802 to the compute service manager 102. Across thedatabase platform, different compute service managers 102 and resourcemanagers 802 may be running different software versions of the databaseplatform at the same time.

As discussed herein, the term “database query manager” may genericallyrefer to either of the compute service manager 102 and/or the resourcemanager 802. The compute service manager 102 and the resource manager802 have similar components and may perform similar tasks and may beinterchangeable in some instances and/or may be particularly assigned tocertain tasks, certain clients, certain portions of the database data,and so forth.

The compute service manager 102 may determine the tasks for retrying thequery at 108 by numerous different methods. The compute service manager102 may receive an indication from a client or system administrator thatthe query should be retried on a certain compute service instance, acertain version of software for the database platform, by a certainexecution platform, and so forth. Alternatively, the compute servicemanager 102 may make these determinations with a query retry module 114.The query retry module 114 may select a compute service instance at 118for identifying and assigning tasks for executing the retry of thequery. The query retry module 114 may select a version of the databaseplatform at 120 on which the query should be retried. The query retrymodule 114 may select an execution platform 122 for performing the retryof the query.

In an embodiment, the compute service manager 102 schedules and managesthe execution of query retries on behalf of a client account. Thecompute service manager 102 may schedule any arbitrary SQL query. Thecompute service manager 102 may assume a role to schedule the failedquery 104 as if it is the client account rather than as an internalaccount or other special account. The compute service manager 102 mayembody the role of, for example, an account administrator or a rolehaving the smallest scope necessary to complete the retry of the failedquery 104.

In an embodiment, the compute service manager 102 determines tasks toretry the query at 108 and assigns those tasks at 110. The computeservice manager 102 may generate one or more discrete units of work thatmay be referred to as a task. The task includes, for example, a tasktype, a task identification, an account identification, a payload whichmay be converted to one or more discrete tasks, and a set of optionsthat control how the failed query 104 is retried (e.g. indicates anumber of retries). In an embodiment, the compute service manager 102identifies one or more micro-partitions of data that need to be read toretry the query. The compute service manager 102 may form batches ofmicro-partitions that need to be read to retry the query and may assignone or more batches to different execution nodes of the executionplatform 116. The compute service manager 102 may determine how manytimes the query should be retried and whether the query should beretried on different compute service instances or on different versionsof the software of the database platform. The compute service manager102 may further determine if the query should be retried on differentexecution nodes or execution platforms. The compute service manager 102may determine how many times the query should be retried and where thequery should be retried based on the reason for the original failure ofthe query. If the reason for the original failure of the query isunknown, then the compute service manager 102 may determine how manytimes and where the query should be retried in an effort to identify whythe query original execution of the query failed.

The compute service manager 102 may assign tasks to retry the query 110.In an embodiment, the compute service manager 102 identifies multiplemicro-partitions of data that need to be read to retry the query andassigns each of the multiple micro-partitions to one or more executionnodes of an execution platform 116. The compute service manager 102 mayassign tasks based on storage and processing availability of themultiple execution nodes of the execution platform. The compute servicemanager 102 may assign tasks in an effort to identify why the originalexecution of the query failed. For example, the compute service manager102 may assign the query retry to the same one or more execution nodesthat originally attempted to execute the query. The compute servicemanager 102 may assign the query retry to new execution nodes that didnot participate in the original failed execution of the query.

The compute service manager 102 analyzes the query retry attempts at 112to identify why the query failed. The compute service manager 102 maydetermine the query failed due to an intermittent fault. Databasequeries can fail due to numerous different intermittent faults that maybe caused by an electrical issue, a circuit issue, change intemperature, vibration, voltage issues, power outages, and so forth. Inan embodiment, the compute service manager 102 may determine that thequery failed due to an intermittent fault but may not identify whatcaused the intermittent fault. In an example, the compute servicemanager 102 may retry the query with the same compute service instance,the same execution nodes, and the same version of the database platformas the original execution attempt for the query. If the query retry issuccessful without changing any of the compute service instance, theexecution nodes, or the version of the database platform, the computeservice manager 102 may deduce that the original execution of the queryfailed due to an intermittent fault.

Alternatively, the compute service manager 102 may determine the queryfailed due to a system issue such as a bug or error in the software orfirmware for the database platform. The compute service manager 102 mayrun the query on the same version of the database platform (i.e., thesame software and code for the database platform) as the original run ofthe query that failed. The computer service manager 102 may additionallyrun the query on other versions of the database platform. The computeservice manager 102 may determine that the query failed due to a systemerror based on the results of the multiple query retries. For example,if the query retry on the same version of the database platform failsagain, and the query retry on a different version of the databaseplatform is executed successfully, then the compute service manager 102may deduce there is an error in the software for the database platformin the version that was used for the original execution of the query.

In an embodiment, the compute service manager 102 generates a reportindicating when a failed query 104 is scheduled to be retried and theamount of computing resources that are estimated to be tied up retryingthe failed query 104. The compute service manager 102 may generate astatement for each task that exposes the failed query 104 to anapplicable client account by way of a filter. The compute servicemanager 102 may alert a client account when a failed query 104 is beingretried.

The query retry module 114 may select a compute service instance at 118to manage the query retry. In an embodiment, the compute service manager102 is one of multiple compute service managers serving a multipletenant cloud-based database platform. The query retry module 114 may beincorporated in the same compute service manager 102 that managed theoriginal, failed execution of the query. The query retry module 114 maydetermine that the query retry should be performed by the same computeservice manager 102 and/or by one or more different compute serviceinstances. In an embodiment, the multiple compute service instancesacross the database platform are running different versions of softwarefor the database platform at any given time. For example, a new versionof the software for the database platform may be rolled out on a selectnumber of compute service instances while other compute serviceinstances continue to run older versions of the software. This gradualrollout can assist in identifying any errors or bugs in the newly rolledout software. The query retry module 114 may determine that the queryshould be retried multiple times by multiple different compute serviceinstances. The query retry module 114 may make this determination in aneffort to identify whether the original execution of the query faileddue to an intermittent fault or a repeating system error.

In an embodiment, the query retry module 114 selects a compute serviceinstance to manage the query retry based on availability of the multiplecompute service instances across the database platform. In anembodiment, the original, failed execution of the query is managed by aresource manager that handles “external” jobs received from clientaccounts such as client-requested queries. The retry of the query maythen be handled by a compute service manager that handles “internal”jobs that are not client-facing. Internal jobs include, for example,query retries, refreshing materialized views, performing adaptiveaggregation of database data, updating metadata, and so forth. Invarious embodiments, the query retry module 114 shown in FIG. 1 may beincorporated in a resource manager and/or a compute service manager andmay be associated with the same resource manager and/or compute servicemanager that oversaw a failed execution of the query.

The query retry module 114 may select a version of the database platformat 120 on which the query retry should be performed. In an embodiment,the systems, methods, and devices disclosed herein are part of amultiple tenant cloud-based database platform. The database platform mayhave multiple resource managers (for executing external jobs) andmultiple compute service managers (for executing internal jobs). Each ofthe multiple resource managers and multiple compute service managerscould be operating under different versions of software for the databaseplatform. In an embodiment, when a new version of software for thedatabase platform is rolled out, the new version is installed in only aselect number of resource managers and/or compute service managers. Theremaining resource managers and/or compute service managers may continueto run older versions of the software for the database platform. Thequery retry module 114 may select one of the multiple available versionsof software for running the query retry.

In an embodiment, the query retry module 114 determines that the queryshould be retried on the same version of software as the original,failed execution attempt. The query retry module 114 may determine thatthe query should additionally be retried on one or more other versionsof software. The results of these multiple retry attempts may beanalyzed to determine whether the original, failed execution of thequery failed due to an intermittent fault or a system error.

The query retry module 114 may select one or more execution nodes orexecution platforms at 122 to perform the one or more retry attempts.The query retry module 114 may select the execution resources based onstorage and/or processing availability and/or current workload. Thequery retry module 114 may select the same execution resources thatattempted to perform the original, failed execution of the query becausethose execution resources may have already cached some portion of datathat is responsive to the query. The query retry module 114 may selectthe same execution resources that attempted to perform the original,failed execution of the query in an effort to identify whether theoriginal execution of the query failed due to an intermittent fault or asystem error. In an embodiment, there is no central query retry module114 that determines the execution resources for multiple retry attempts,and instead these determinations are made by each individual computeservice instance that is assigning tasks for executing the query retry.

In an embodiment, the query retry module 114 selects one or moreexecution nodes to perform one or more retry attempts on the query. Thequery retry module 114 may determine that a query will be retriedmultiple times using different execution nodes. A first retry executionof the query may be performed on the same execution nodes that attemptedthe original, failed execution of the query. A second retry execution ofthe query may be performed on different execution nodes that did notattempt the original, failed execution of the query. A third retryexecution of the query may be performed by a mixture of execution nodesthat did and did not attempt the original, failed execution of thequery. The query retry module 114 may schedule multiple retries of thequery until the query or is successful and/or a cause of the originalfailure of the query has been identified. For example, it may bedetermined that the original failure of the query was caused by ahardware issue or a problem with a specific server that was involved inthe original execution of the query. This determination may be made byperforming multiple retries of the query using multiple differentexecution nodes.

In an embodiment, the query retry module 114 determines at 122 that thequery should be retried on the same execution nodes that attempted toexecute the original, failed execution of the query. The result of thisretry attempt may be analyzed to determine if there is a hardware issuewith a specific server. If the query retry fails on the same executionnodes used for the original, failed execution of the query, this mayindicate that further investigation should be made to determine whetherthere is an issue with the server running those one or more executionnodes.

In an embodiment, the query retry module 114 first selects one or moreversions of the database platform at 120 to perform the one or morequery retries. The query retry module 114 may then select one or morecompute service instances at 118 to manage the one or more queryretries. The query retry module 114 may select compute service instancesbased on which version of the database platform the compute serviceinstances are currently running.

The query retry module 114 may be incorporated into a compute servicemanager 102 as shown in FIG. 1. Alternatively, the query retry module114 may be separate from any compute service instance and may beconfigured to make determinations about query retry attempts for one ormore accounts of a multiple tenant database platform. A separateinstance of the query retry module 114 may be incorporated in eachcompute service instance. A single instance of the query retry module114 may be incorporated in a single compute service instance and maymake determinations about query retry attempts for one or more accountsof a multiple tenant database platform.

Modifications may be made to the process flow 100 in FIG. 1 withoutdeparting from the scope of the disclosure. For example, thedeterminations made by the query retry module 114 may be made by acompute service manager 102 that received a query retry job in itsqueue. The determinations made by the query retry module 114 may be madeby the resource manager that managed the original, failed execution ofthe query. The determinations made by the query retry module 114 may bemade by some other resource manager 802 and/or compute service manager102.

In an embodiment, the resource manager 802 that managed the original,failed execution of the query further determines whether the query canbe retried, whether the query should be retried, where the query shouldbe retried, on which version of the database platform the query shouldbe retried, and which compute service manager should manage the retry ofthe query. The resource manager 802 may transfer the query to theappropriate compute service manager based on these determinations. In anembodiment, a compute service manager that oversees a retry attempt ofthe query may generate additional retry attempts based on whether theretry attempted was successful or unsuccessful. In an embodiment, theresource manager 802 that managed the original, failed execution of thequery may transfer the query to multiple different compute servicemanagers for the query to be retried multiple times. The resourcemanager 802 may determine that the query should be retried on multipleversions of the database platform, on multiple different executionnodes, and by multiple compute service managers and/or resourcemanagers.

In an embodiment, a transaction log is stored in a metadata store and/oracross one or more of a plurality of shared storage devices in adatabase platform. The transaction log may comprise a listing of all thejobs that have been performed for a client account. The transaction logmay include a listing of each query retry attempt for a single query. Inan embodiment, the client account may request the transaction log and afiltered transaction log may be generated that omits the query retryattempts and comprises only an indication of the original execution ofthe query and/or a successful retry attempt for the query. The filteredtransaction log may be provided to the client account. In an embodiment,the transaction log comprises a listing of all “external” jobs that wereperformed based on direct request from the client account and omits all“internal” jobs that are done for improving performance of the databaseplatform and are not received from the client account. In an embodiment,the client account may request a specialized transaction log thatcomprises a listing of all external and/or internal jobs the clientaccount wishes to see. In an embodiment, a transaction log is generatedthat comprises a listing of all attempts to execute a single query, allqueries over a time period, all queries directed at certain databasedata, and so forth.

FIG. 2 is a block diagram depicting an example embodiment of a dataprocessing platform 200. As shown in FIG. 2, a compute service manager102 is in communication with a queue 204, a client account 208, metadata206, and an execution platform 116. In an embodiment, the computeservice manager 102 does not receive any direct communications from aclient account 208 and only receives communications concerning jobs fromthe queue 204. The jobs in the queue 204 may include, for example,retrying a failed query, refreshing a materialized view, refreshing anaggregation, reclustering a table, and so forth. In particularimplementations, the compute service manager 102 can support any numberof client accounts 208 such as end users providing data storage andretrieval requests, system administrators managing the systems andmethods described herein, and other components/devices that interactwith compute service manager 102. As used herein, compute servicemanager 102 may also be referred to as a “global services system” thatperforms various functions as discussed herein.

The compute service manager 102 is in communication with a queue 204.The queue 204 may provide a job to the compute service manager 102 inresponse to a trigger event. In an embodiment, the trigger event is afailed query and the job is a retry of the failed query. In anembodiment, the resource manager 802 that managed the original, failedexecution of the query is configured to enter a job in the queue 204indicating that the compute service manager 102 should retry the query.This decoupling of external tasks (e.g., queries received from clientaccounts) and internal tasks (e.g., retrying queries) can ensure thatresource managers 802 are available to receive client requests and thatprocessing resources are not consumed on internal tasks while externaltasks are waiting. One or more jobs may be stored in the queue 204 in anorder of receipt and/or an order of priority, and each of those one ormore jobs may be communicated to the compute service manager 102 to bescheduled and executed. The queue 204 may determine a job to beperformed based on a trigger event such as the failure of a query,ingestion of data, deleting one or more rows in a table, updating one ormore rows in a table, a materialized view becoming stale with respect toits source table, a table reaching a predefined clustering thresholdindicating the table should be reclustered, and so forth. The queue 204may determine internal jobs that should be performed to improve theperformance of the database and/or to improve the organization ofdatabase data. In an embodiment, the queue 204 does not store queries tobe executed for a client account but instead only includes database jobsthat improve database performance.

The compute service manager 102 is also coupled to metadata 206, whichis associated with the entirety of data stored throughout dataprocessing platform 200. In some embodiments, metadata 206 includes asummary of data stored in remote data storage systems as well as dataavailable from a local cache. Additionally, metadata 206 may includeinformation regarding how data is organized in the remote data storagesystems and the local caches. Metadata 206 allows systems and servicesto determine whether a piece of data needs to be accessed withoutloading or accessing the actual data from a storage device.

In an embodiment, the compute service manager 102 and/or the queue 204may determine that a job should be performed based on the metadata 206.In such an embodiment, the compute service manager 102 and/or the queue204 may scan the metadata 206 and determine that a job should beperformed to improve data organization or database performance.

The compute service manager 102 may receive rules or parameters from theclient account 208 and such rules or parameters may guide the computeservice manager 102 in scheduling and managing internal jobs. The clientaccount 208 may indicate that internal jobs should only be executed atcertain times or should only utilize a set maximum amount of processingresources. The client account 208 may further indicate one or moretrigger events that should prompt the compute service manager 102 todetermine that a job should be performed. The client account 208 mayprovide parameters concerning how many times a task may be re-executedand/or when the task should be re-executed. In an embodiment, thecompute service manager 102 is configured to prioritize query retriesover other internal tasks.

The compute service manager 102 is further coupled to an executionplatform 116, which provides multiple computing resources that executevarious data storage and data retrieval tasks, as discussed in greaterdetail below. Execution platform 116 is coupled to multiple data storagedevices 212 a, 212 b, and 212 n that are part of a storage platform 210.Although three data storage devices 212 a, 212 b, and 212 n are shown inFIG. 2, execution platform 116 is capable of communicating with anynumber of data storage devices. In some embodiments, data storagedevices 212 a, 212 b, and 212 n are cloud-based storage devices locatedin one or more geographic locations. For example, data storage devices212 a, 212 b, and 212 n may be part of a public cloud infrastructure ora private cloud infrastructure. Data storage devices 212 a, 212 b, and212 n may be hard disk drives (HDDs), solid state drives (SSDs), storageclusters, Amazon S3™ storage systems or any other data storagetechnology. Additionally, storage platform 210 may include distributedfile systems (such as Hadoop Distributed File Systems (HDFS)), objectstorage systems, and the like.

In particular embodiments, the communication links between computeservice manager 102, the queue 204, metadata 206, the client account208, and the execution platform 116 are implemented via one or more datacommunication networks. Similarly, the communication links betweenexecution platform 116 and data storage devices 212 a-212 n in thestorage platform 210 are implemented via one or more data communicationnetworks. These data communication networks may utilize anycommunication protocol and any type of communication medium. In someembodiments, the data communication networks are a combination of two ormore data communication networks (or sub-networks) coupled to oneanother. In alternate embodiments, these communication links areimplemented using any type of communication medium and any communicationprotocol.

As shown in FIG. 2, data storage devices 212 a, 212 b, and 212 n aredecoupled from the computing resources associated with the executionplatform 116. This architecture supports dynamic changes to dataprocessing platform 200 based on the changing data storage/retrievalneeds as well as the changing needs of the users and systems accessingdata processing platform 200. The support of dynamic changes allows dataprocessing platform 200 to scale quickly in response to changing demandson the systems and components within data processing platform 200. Thedecoupling of the computing resources from the data storage devicessupports the storage of large amounts of data without requiring acorresponding large amount of computing resources. Similarly, thisdecoupling of resources supports a significant increase in the computingresources utilized at a particular time without requiring acorresponding increase in the available data storage resources.

Compute service manager 102, queue 204, metadata 206, client account208, execution platform 116, and storage platform 210 are shown in FIG.2 as individual components. However, each of compute service manager102, queue 204, metadata 206, client account 208, execution platform116, and storage platform 210 may be implemented as a distributed system(e.g., distributed across multiple systems/platforms at multiplegeographic locations). Additionally, each of compute service manager102, metadata 206, execution platform 116, and storage platform 210 canbe scaled up or down (independently of one another) depending on changesto the requests received from the queue 204 and/or client accounts 208and the changing needs of data processing platform 200. Thus, in thedescribed embodiments, data processing platform 200 is dynamic andsupports regular changes to meet the current data processing needs.

During typical operation, data processing platform 200 processesmultiple jobs received from the queue 204 or determined by the computeservice manager 102. These jobs are scheduled and managed by the computeservice manager 102 to determine when and how to execute the job. Forexample, the compute service manager 102 may divide the job intomultiple discrete tasks and may determine what data is needed to executeeach of the multiple discrete tasks. The compute service manager 102 mayassign each of the multiple discrete tasks to one or more nodes of theexecution platform 116 to process the task. The compute service manager102 may determine what data is needed to process a task and furtherdetermine which nodes within the execution platform 116 are best suitedto process the task. Some nodes may have already cached the data neededto process the task and, therefore, be a good candidate for processingthe task. Metadata 206 assists the compute service manager 102 indetermining which nodes in the execution platform 116 have alreadycached at least a portion of the data needed to process the task. One ormore nodes in the execution platform 116 process the tasks using datacached by the nodes and, if necessary, data retrieved from the storageplatform 210. It is desirable to retrieve as much data as possible fromcaches within the execution platform 116 because the retrieval speed istypically much faster than retrieving data from the storage platform210.

As shown in FIG. 2, the data processing platform 200 separates theexecution platform 116 from the storage platform 210. In thisarrangement, the processing resources and cache resources in theexecution platform 116 operate independently of the data storageresources 212 a-212 n in the storage platform 210. Thus, the computingresources and cache resources are not restricted to specific datastorage resources 212 a-212 n. Instead, all computing resources and allcache resources may retrieve data from, and store data to, any of thedata storage resources in the storage platform 210. Additionally, thedata processing platform 200 supports the addition of new computingresources and cache resources to the execution platform 116 withoutrequiring any changes to the storage platform 210. Similarly, the dataprocessing platform 200 supports the addition of data storage resourcesto the storage platform 210 without requiring any changes to nodes inthe execution platform 116.

FIG. 3 is a block diagram depicting an embodiment of the compute servicemanager 102. As shown in FIG. 3, the compute service manager 102includes an access manager 302 and a key manager 304 coupled to a datastorage device 306. Access manager 302 handles authentication andauthorization tasks for the systems described herein. Key manager 304manages storage and authentication of keys used during authenticationand authorization tasks. For example, access manager 302 and key manager304 manage the keys used to access data stored in remote storage devices(e.g., data storage devices in storage platform 210). As used herein,the remote storage devices may also be referred to as “persistentstorage devices” or “shared storage devices.” A request processingservice 308 manages received data storage requests and data retrievalrequests (e.g., jobs to be performed on database data). For example, therequest processing service 308 may determine the data necessary toprocess the received data storage request or data retrieval request. Thenecessary data may be stored in a cache within the execution platform116 (as discussed in greater detail below) or in a data storage devicein storage platform 210. A management console service 310 supportsaccess to various systems and processes by administrators and othersystem managers. Additionally, the management console service 310 mayreceive a request to execute a job and monitor the workload on thesystem.

The compute service manager 102 also includes a job compiler 312, a joboptimizer 314 and a job executor 310. The job compiler 312 parses a jobinto multiple discrete tasks and generates the execution code for eachof the multiple discrete tasks. The job optimizer 314 determines thebest method to execute the multiple discrete tasks based on the datathat needs to be processed. The job optimizer 314 also handles variousdata pruning operations and other data optimization techniques toimprove the speed and efficiency of executing the job. The job executor316 executes the execution code for jobs received from the queue 204 ordetermined by the compute service manager 102.

A job scheduler and coordinator 318 sends received jobs to theappropriate services or systems for compilation, optimization, anddispatch to the execution platform 116. For example, jobs may beprioritized and processed in that prioritized order. In an embodiment,the job scheduler and coordinator 318 determines a priority for internaljobs that are scheduled by the compute service manager 102 with other“outside” jobs such as user queries that may be scheduled by othersystems in the database but may utilize the same processing resources inthe execution platform 116. In some embodiments, the job scheduler andcoordinator 318 identifies or assigns particular nodes in the executionplatform 116 to process particular tasks. A virtual warehouse manager320 manages the operation of multiple virtual warehouses implemented inthe execution platform 116. As discussed below, each virtual warehouseincludes multiple execution nodes that each include a cache and aprocessor.

Additionally, the compute service manager 102 includes a configurationand metadata manager 322, which manages the information related to thedata stored in the remote data storage devices and in the local caches(i.e., the caches in execution platform 116). As discussed in greaterdetail below, the configuration and metadata manager 322 uses themetadata to determine which data files need to be accessed to retrievedata for processing a particular task or job. A monitor and workloadanalyzer 324 oversees processes performed by the compute service manager102 and manages the distribution of tasks (e.g., workload) across thevirtual warehouses and execution nodes in the execution platform 116.The monitor and workload analyzer 324 also redistributes tasks, asneeded, based on changing workloads throughout the data processingplatform 200 and may further redistribute tasks based on a user (i.e.“external”) query workload that may also be processed by the executionplatform 116. The configuration and metadata manager 322 and the monitorand workload analyzer 324 are coupled to a data storage device 326. Datastorage devices 306 and 326 in FIG. 3 represent any data storage devicewithin data processing platform 200. For example, data storage devices306 and 326 may represent caches in execution platform 116, storagedevices in storage platform 210, or any other storage device.

The compute service manager 102 also includes a transaction managementand access control module 328, which manages the various tasks and otheractivities associated with the processing of data storage requests anddata access requests. For example, transaction management and accesscontrol module 328 provides consistent and synchronized access to databy multiple users or systems. Since multiple users/systems may accessthe same data simultaneously, changes to the data must be synchronizedto ensure that each user/system is working with the current version ofthe data. Transaction management and access control module 328 providescontrol of various data processing activities at a single, centralizedlocation in the compute service manager 102. In some embodiments, thetransaction management and access control module 328 interacts with thejob executor 316 to support the management of various tasks beingexecuted by the job executor 316.

FIG. 4 is a block diagram depicting an embodiment of an executionplatform 116. As shown in FIG. 4, execution platform 116 includesmultiple virtual warehouses, including virtual warehouse 1, virtualwarehouse 2, and virtual warehouse n. Each virtual warehouse includesmultiple execution nodes that each include a data cache and a processor.The virtual warehouses can execute multiple tasks in parallel by usingthe multiple execution nodes. As discussed herein, execution platform116 can add new virtual warehouses and drop existing virtual warehousesin real-time based on the current processing needs of the systems andusers. This flexibility allows the execution platform 116 to quicklydeploy large amounts of computing resources when needed without beingforced to continue paying for those computing resources when they are nolonger needed. All virtual warehouses can access data from any datastorage device (e.g., any storage device in storage platform 210).

Although each virtual warehouse shown in FIG. 4 includes three executionnodes, a particular virtual warehouse may include any number ofexecution nodes. Further, the number of execution nodes in a virtualwarehouse is dynamic, such that new execution nodes are created whenadditional demand is present, and existing execution nodes are deletedwhen they are no longer necessary.

Each virtual warehouse is capable of accessing any of the data storagedevices 310 a-310 n shown in FIG. 3. Thus, the virtual warehouses arenot necessarily assigned to a specific data storage device 212 a-212 nand, instead, can access data from any of the data storage devices 212a-212 n within the storage platform 210. Similarly, each of theexecution nodes shown in FIG. 4 can access data from any of the datastorage devices 212 a-212 n. In some embodiments, a particular virtualwarehouse or a particular execution node may be temporarily assigned toa specific data storage device, but the virtual warehouse or executionnode may later access data from any other data storage device.

In the example of FIG. 4, virtual warehouse 1 includes three executionnodes 402 a, 402 b, and 402 n. Execution node 402 a includes a cache 404a and a processor 406 a. Execution node 402 b includes a cache 404 b anda processor 406 b. Execution node 402 n includes a cache 404 n and aprocessor 406 n. Each execution node 402 a, 402 b, and 402 n isassociated with processing one or more data storage and/or dataretrieval tasks. For example, a virtual warehouse may handle datastorage and data retrieval tasks associated with an internal service,such as a clustering service, a materialized view refresh service, afile compaction service, a storage procedure service, or a file upgradeservice. In other implementations, a particular virtual warehouse mayhandle data storage and data retrieval tasks associated with aparticular data storage system or a particular category of data.

Similar to virtual warehouse 1 discussed above, virtual warehouse 2includes three execution nodes 412 a, 412 b, and 412 n. Execution node412 a includes a cache 414 a and a processor 416 a. Execution node 412 bincludes a cache 414 b and a processor 416 b. Execution node 412 nincludes a cache 414 n and a processor 416 n. Additionally, virtualwarehouse 3 includes three execution nodes 422 a, 422 b, and 422 n.Execution node 422 a includes a cache 424 a and a processor 426 a.Execution node 422 b includes a cache 424 b and a processor 426 b.Execution node 422 n includes a cache 424 n and a processor 426 n.

In some embodiments, the execution nodes shown in FIG. 4 are statelesswith respect to the data the execution nodes are caching. For example,these execution nodes do not store or otherwise maintain stateinformation about the execution node, or the data being cached by aparticular execution node. Thus, in the event of an execution nodefailure, the failed node can be transparently replaced by another node.Since there is no state information associated with the failed executionnode, the new (replacement) execution node can easily replace the failednode without concern for recreating a particular state.

Although the execution nodes shown in FIG. 4 each include one data cacheand one processor, alternate embodiments may include execution nodescontaining any number of processors and any number of caches.Additionally, the caches may vary in size among the different executionnodes. The caches shown in FIG. 4 store, in the local execution node,data that was retrieved from one or more data storage devices in storageplatform 210. Thus, the caches reduce or eliminate the bottleneckproblems occurring in platforms that consistently retrieve data fromremote storage systems. Instead of repeatedly accessing data from theremote storage devices, the systems and methods described herein accessdata from the caches in the execution nodes which is significantlyfaster and avoids the bottleneck problem discussed above. In someembodiments, the caches are implemented using high-speed memory devicesthat provide fast access to the cached data. Each cache can store datafrom any of the storage devices in the storage platform 210.

Further, the cache resources and computing resources may vary betweendifferent execution nodes. For example, one execution node may containsignificant computing resources and minimal cache resources, making theexecution node useful for tasks that require significant computingresources. Another execution node may contain significant cacheresources and minimal computing resources, making this execution nodeuseful for tasks that require caching of large amounts of data. Yetanother execution node may contain cache resources providing fasterinput-output operations, useful for tasks that require fast scanning oflarge amounts of data. In some embodiments, the cache resources andcomputing resources associated with a particular execution node aredetermined when the execution node is created, based on the expectedtasks to be performed by the execution node.

Additionally, the cache resources and computing resources associatedwith a particular execution node may change over time based on changingtasks performed by the execution node. For example, an execution nodemay be assigned more processing resources if the tasks performed by theexecution node become more processor-intensive. Similarly, an executionnode may be assigned more cache resources if the tasks performed by theexecution node require a larger cache capacity.

Although virtual warehouses 1, 2, and n are associated with the sameexecution platform 116, the virtual warehouses may be implemented usingmultiple computing systems at multiple geographic locations. Forexample, virtual warehouse 1 can be implemented by a computing system ata first geographic location, while virtual warehouses 2 and n areimplemented by another computing system at a second geographic location.In some embodiments, these different computing systems are cloud-basedcomputing systems maintained by one or more different entities.

Additionally, each virtual warehouse is shown in FIG. 4 as havingmultiple execution nodes. The multiple execution nodes associated witheach virtual warehouse may be implemented using multiple computingsystems at multiple geographic locations. For example, an instance ofvirtual warehouse 1 implements execution nodes 402 a and 402 b on onecomputing platform at a geographic location and implements executionnode 402 n at a different computing platform at another geographiclocation. Selecting particular computing systems to implement anexecution node may depend on various factors, such as the level ofresources needed for a particular execution node (e.g., processingresource requirements and cache requirements), the resources availableat particular computing systems, communication capabilities of networkswithin a geographic location or between geographic locations, and whichcomputing systems are already implementing other execution nodes in thevirtual warehouse.

Execution platform 116 is also fault tolerant. For example, if onevirtual warehouse fails, that virtual warehouse is quickly replaced witha different virtual warehouse at a different geographic location.

A particular execution platform 116 may include any number of virtualwarehouses. Additionally, the number of virtual warehouses in aparticular execution platform is dynamic, such that new virtualwarehouses are created when additional processing and/or cachingresources are needed. Similarly, existing virtual warehouses may bedeleted when the resources associated with the virtual warehouse are nolonger necessary.

In some embodiments, the virtual warehouses may operate on the same datain storage platform 210, but each virtual warehouse has its ownexecution nodes with independent processing and caching resources. Thisconfiguration allows requests on different virtual warehouses to beprocessed independently and with no interference between the requests.This independent processing, combined with the ability to dynamicallyadd and remove virtual warehouses, supports the addition of newprocessing capacity for new users without impacting the performanceobserved by the existing users.

FIG. 5 is a block diagram depicting an example operating environment 500with the queue 204 in communication with multiple virtual warehousesunder a virtual warehouse manager 502. In environment 500, the queue 204has access to multiple database shared storage devices 506 a, 506 b, 506c, 506 d, 506 e and 506 n through multiple virtual warehouses 504 a, 504b, and 504 n. Although not shown in FIG. 5, the queue 204 may accessvirtual warehouses 504 a, 504 b, and 504 n through the compute servicemanager 102. In particular embodiments, databases 506 a-506 n arecontained in the storage platform 210 and are accessible by any virtualwarehouse implemented in the execution platform 116. In someembodiments, the queue 204 may access one of the virtual warehouses 504a-504 n using a data communication network such as the Internet. In someimplementations, a client account may specify that the queue 204(configured for storing internal jobs to be completed) should interactwith a particular virtual warehouse 504 a-504 n at a particular time.

In an embodiment (as illustrated), each virtual warehouse 504 a-504 ncan communicate with all databases 506 a-506 n. In some embodiments,each virtual warehouse 504 a-504 n is configured to communicate with asubset of all databases 506 a-506 n. In such an arrangement, anindividual client account associated with a set of data may send alldata retrieval and data storage requests through a single virtualwarehouse and/or to a certain subset of the databases 506 a-506 n.Further, where a certain virtual warehouse 504 a-504 n is configured tocommunicate with a specific subset of databases 506 a-506 n, theconfiguration is dynamic. For example, virtual warehouse 504 a may beconfigured to communicate with a first subset of databases 506 a-506 nand may later be reconfigured to communicate with a second subset ofdatabases 506 a-506 n.

In an embodiment, the queue 204 sends data retrieval, data storage, anddata processing requests to the virtual warehouse manager 502, whichroutes the requests to an appropriate virtual warehouse 504 a-504 n. Insome implementations, the virtual warehouse manager 502 provides adynamic assignment of jobs to the virtual warehouses 504 a-504 n.

In some embodiments, fault tolerance systems create a new virtualwarehouse in response to a failure of a virtual warehouse. The newvirtual warehouse may be in the same virtual warehouse group or may becreated in a different virtual warehouse group at a different geographiclocation.

The systems and methods described herein allow data to be stored andaccessed as a service that is separate from computing (or processing)resources. Even if no computing resources have been allocated from theexecution platform 116, data is available to a virtual warehouse withoutrequiring reloading of the data from a remote data source. Thus, data isavailable independently of the allocation of computing resourcesassociated with the data. The described systems and methods are usefulwith any type of data. In particular embodiments, data is stored in astructured, optimized format. The decoupling of the data storage/accessservice from the computing services also simplifies the sharing of dataamong different users and groups. As discussed herein, each virtualwarehouse can access any data to which it has access permissions, evenat the same time as other virtual warehouses are accessing the samedata. This architecture supports running queries without any actual datastored in the local cache. The systems and methods described herein arecapable of transparent dynamic data movement, which moves data from aremote storage device to a local cache, as needed, in a manner that istransparent to the user of the system. Further, this architecturesupports data sharing without prior data movement since any virtualwarehouse can access any data due to the decoupling of the data storageservice from the computing service.

FIG. 6 is a diagram of a process flow 600 for retrying a failed query104. The process flow 600 begins with an indication of a failed query104. That indication of the failed query 104 may be a positive/negativemessage that indicates only that the attempt to execute the query failedand does not provide any indication of how or when the execution of thequery failed. In response to the indication of the failed query 104, acompute service manager 102 or other computing resource may schedule thequery to be retried at 602 on the same version of the database platformthat was used for the original, failed execution of the query. Theversion of the database platform may be a collection of software orfirmware code that indicates how the database platform should run. Ifthe retry of the query is successful at 604, then the result of thequery is returned to the client at 610. If the retry of the query issuccessful at 604, then it can be presumed that the original, failedexecution of the query failed due to an intermittent fault. If the retryof the query is unsuccessful at 604, then a compute service manager 102or other computing resource may schedule the query to be retried at 606on some other version of the database platform that was not used for theoriginal, failed execution of the query. If the retry of the query issuccessful at 608, then the query result may be returned to the clientat 610. In an embodiment, after one or more attempts at the query orunsuccessful, an indication of an internal failure is sent to the clientaccount. If the retry of the query is unsuccessful at 608, then thequery may again be retried on some other version of the databaseplatform or on the same version of the database platform. The query maybe retried repeatedly until execution of the query is successful. Thequery may be run on any number of versions of the database platform.

If execution of the query is unsuccessful at 604 after being retried onthe same version of the database platform at 602, and if the executionof the query is successful at 608 after being retried on some otherversion of the database platform at 606, then the original, failedexecution of the query may have failed due to a system error rather thanan intermittent fault. The query may be retried multiple times until adetermination can be made that the query is failing due to anintermittent fault or the query is failing due to a system error. Thesystem error may include an issue or bug in the software or firmwarethat supports the database platform. The multiple query retry attemptsmay be leveraged to identify bugs or errors in the software for thedatabase platform.

FIG. 7 is a process flow 700 for a query retry run. In an embodiment,the default mode for the query retry system is batch-oriented where afailed query 104 is selected up front and then run under differentsettings for comparison. This batch-oriented mode may work well forsmall to medium sized queries but may not work well for larger queries.Larger queries pose several problems. One problem posed by largerqueries is that they take longer to run, and, even in the absence of aschema change, clients are constantly ingesting new data into thedatabase. To enable results comparisons, the query retry system may usea fixed time travel version for each query retry which is typically setat the beginning of the run. When running a query in real-time on behalfof a client account, it is acceptable for the query to appear to run atany single arbitrary point after the client account first submitted thequery request and before the client account receives the final response.This ensures that query executions are linearizable. That is, when aquery is retried, it may appear the database sat idle for a long timebefore starting to execute the query (and in the meantime additionaldata may be ingested by other processes or client account), but this isstill a correct execution.

The process flow 700 for the streaming mode addresses the aforementionedproblems with the batch-oriented process flow. In the streaming modeprocess flow 700, the query retry module 114 determines queryconfiguration and run settings. In an embodiment, failed queries 104 torerun are selected incrementally by the workload selector 702 and addedto a query queue 704 which then submits queries to the query retryrunner 706. The query retry runner 706 takes in each query andmultiplexes it to run with different settings before performingverification and comparison to generate the report 714. The query retryrunner 706 will run the baseline run 708 and the target run 710according to different parameters that may be determined by the queryretry runner 706 or input by the query retry module 114. The results ofthe streaming runs may be periodically flushed to shared storage devicesin the database system so that users may poll the latest results from anongoing streaming run.

The baseline run 708 may have the same settings as the original, failedexecution of the query. For example, the baseline run 708 may be managedby the same compute service instance, may be performed on the sameversion of the database platform, and may be executed by the sameexecution nodes as the original, failed execution of the query. Thetarget run 710 may have one or more adjustments made relative to theoriginal, failed execution of the query. For example, the target run 710may be managed by a different compute service instance than theoriginal, failed execution of the query. The target run 710 may beperformed on a different version of the database platform than theoriginal, failed execution of the query. The target run 710 may beexecuted by different execution nodes than the original, failedexecution of the query.

Referring now to FIG. 8, a computer system 800 is illustrated forrunning some of the methods disclosed herein. The computer system 800may work with the data processing platform 200 to schedule, manage, andexecute all tasks for the database platform. In an embodiment, thecompute service manager 102 illustrated in FIG. 2 is configured tomanage “internal” database tasks stored in a queue 204. Such tasks arenot received from a client account and are performed for the purpose ofimproving database operations. The resource manager 802 shown in FIG. 8is configured to manage “external” database tasks received from a clientaccount such as a query request. Each of the compute service manager 102and the resource manager 802 may be connected to the same storageplatform 210, execution platform 116, and metadata 206 store. Theresource manager 802 may be configured to receive a query request from aclient account and manage the original execution of that query. If theoriginal execution of the query fails, then the resource manager 802 mayentry a retry request for the query in a queue 204 to be managed by thecompute service manager 102. In an embodiment, the resource manager 802includes all of the same components and modules as the compute servicemanager 102 as illustrated in FIG. 3.

As shown in FIG. 8, resource manager 802 may be coupled to multipleusers 804, 806, 808. In particular implementations, resource manager 802can support any number of users desiring access to the data processingplatform 300. Users 804, 806, 808 may include, for example, end usersproviding data storage and retrieval requests, system administratorsmanaging the systems and methods described herein, and othercomponents/devices that interact with resource manager 802. The users804, 806, 808 may be referred to herein as “clients” and may have adirect connection to one or more deployments as disclosed herein. Eachof the users 804, 806, 808 may be connected to a primary deployment andhave the capability to transition the connection from the primarydeployment to a secondary deployment.

The resource manager 802 may be coupled to the metadata 206 store, whichis associated with the entirety of data stored throughout dataprocessing platform 300. In some embodiments, metadata 206 may include asummary of data stored in remote data storage systems as well as dataavailable from a local cache. Additionally, metadata 206 may includeinformation regarding how data is organized in the remote data storagesystems and the local caches. Metadata 206 may allow systems andservices to determine whether a piece of data needs to be processedwithout loading or accessing the actual data from a storage device.

Resource manager 802 may be further coupled to the execution platform116, which provides multiple computing resources that execute variousdata storage and data retrieval tasks, as discussed in greater detailbelow. In an embodiment, there exists one or more execution platforms116 used for executing client tasks, such as database queries and/or“internal” database tasks such as updating metadata, clustering a table,generating a materialized view, and so forth. In such an embodiment,there may also exist one or more execution platforms 116 used forincremental feature development and/or testing, and those executionplatforms 116 are separate from the client execution platforms 116 suchthat client processing is not impacted by feature development tasks.Execution platform 116 may be coupled to multiple data storage devices212 a, 212 b, 212 n that are part of a storage platform 210. Althoughthree data storage devices 212 a, 212 b, 212 n are shown in FIG. 8,execution platform 116 is capable of communicating with any number ofdata storage devices. In some embodiments, data storage devices 212 a,212 b, 212 n are cloud-based storage devices located in one or moregeographic locations. For example, data storage devices 212 a, 212 b,212 n may be part of a public cloud infrastructure or a private cloudinfrastructure. Data storage devices 212 a, 212 b, 212 n may be harddisk drives (HDDs), solid state drives (SSDs), storage clusters or anyother data storage technology. Additionally, storage platform 210 mayinclude distributed file systems (such as Hadoop Distributed FileSystems (HDFS)), object storage systems, and the like.

In particular embodiments, the communication links between resourcemanager 802 and users 804, 806, 808, metadata 206, and executionplatform 116 are implemented via one or more data communicationnetworks. Similarly, the communication links between execution platform116 and data storage devices 212 a, 212 b, 212 n in storage platform 210are implemented via one or more data communication networks. These datacommunication networks may utilize any communication protocol and anytype of communication medium. In some embodiments, the datacommunication networks are a combination of two or more datacommunication networks (or sub-networks) coupled to one another. Inalternate embodiments, these communication links are implemented usingany type of communication medium and any communication protocol.

As shown in FIG. 8, data storage devices 212 a, 212 b, 212 n aredecoupled from the computing resources associated with executionplatform 116. In an embodiment, each of a plurality of databasedeployments may include storage platform 210 having multiple datastorage devices 212 a, 212 b, 212 n. Each of the storage platforms 314across the multiple deployments may store a replica of the database datasuch that each of the multiple deployments is capable of serving as aprimary deployment where updates and queries are executed on thedatabase data. This architecture supports dynamic changes to dataprocessing platform 800 based on the changing data storage/retrievalneeds as well as the changing needs of the users and systems accessingdata processing platform 800. The support of dynamic changes allows dataprocessing platform 800 to scale quickly in response to changing demandson the systems and components within data processing platform 800. Thedecoupling of the computing resources from the data storage devicessupports the storage of large amounts of data without requiring acorresponding large amount of computing resources. Similarly, thisdecoupling of resources supports a significant increase in the computingresources utilized at a particular time without requiring acorresponding increase in the available data storage resources.

Resource manager 802, metadata 206, execution platform 116, and storageplatform 210 are shown in FIG. 8 as individual components. However, eachof resource manager 802, metadata 206, execution platform 116, andstorage platform 210 may be implemented as a distributed system (e.g.,distributed across multiple systems/platforms at multiple geographiclocations). Additionally, each of resource manager 802, metadata 206,execution platform 116, and storage platform 210 can be scaled up ordown (independently of one another) depending on changes to the requestsreceived from users 804, 806, 808 and the changing needs of dataprocessing platform 800. Thus, data processing platform 800 is dynamicand supports regular changes to meet the current data processing needs.

In various implementations of the disclosure, attempts to retry a querymay be managed by a resource manager 802 and/or a compute servicemanager 102. In an embodiment, an original query request is received bythe resource manager 802 from a client account, and the resource manager802 manages the original attempt to execute the query. The resourcemanager 802 may pass the query on to a compute service manager 102 tomanage one or more attempts to retry the query. Alternatively, the sameresource manager 802 that managed the original, failed execution of thequery may also manage one or more attempts to retry the query.

In an embodiment, the resource manager 802 is configured to assign aunique identification number to each query that is received from theusers 804, 806, 808. The unique identification number enables therequesting user and/or client account to access and read the query andthe query results. In an embodiment, when the original execution of thequery fails and a retry execution of the query is successful, the uniqueidentification may be altered to point to the retry execution of thequery rather than the original execution of the query. In an embodiment,the unique identification number is used to determine a Uniform ResourceLocator (URL) address where a client account may access the query and/orthe query results.

FIG. 9 is a schematic flow chart diagram of a method 900 for retrying aquery. The method 900 may be executed by any suitable computingresources such as a compute service manager 102, a query retry module114, and/or a resource manager 802. The method 900 may be executed byone or more database query managers which may generically refer toeither of the compute service manager 102 and/or the resource manager802.

The method 900 begins and the computing resource receives at 902 a querydirected to database data. A computing resource assigns at 904 executionof the query to one or more execution nodes of an execution platform,wherein the one or more execution nodes are configured to execute thequery on a first version of a database platform. The method 900continues and a computing resource determines at 906 that execution ofthe query was unsuccessful. A computing resource assigns at 908 a retryof the query on the first version of the database platform. A computingresource assigns at 910 a retry of the query on a second version of thedatabase platform. A computing resource may assign the retries of thequery at 908 and 910 to the same one or more execution nodes of theexecution platform and/or to other execution nodes of other executionplatforms. The first version and the second version of the databaseplatform may be versions of software or firmware that control andoptimize operations for the database platform, including operations forthe execution of a query.

FIG. 10 is a schematic flow chart diagram of a method 1000 for retryinga query. The method 1000 may be executed by any suitable computingresources such as a compute service manager 102, a query retry module114, and/or a resource manager 802. The method 1000 may be executed byone or more database query managers which may generically refer toeither of the compute service manager 102 and/or the resource manager802.

The method 1000 begins and a computing resource receives at 1002 a querydirected to database data. A computing resource assigns at 1004execution of the query to one or more execution nodes of an executionplatform, wherein the one or more execution nodes are configured toexecute the query on a first version of a database platform. The method1000 continues and a computing resource determines at 1006 thatexecution of the query was unsuccessful. A computing resource assigns at1008 a retry of the execution of the query to the one or more executionnodes of the execution platform. The method 1000 continues and acomputing resource determines at 1010 whether a regression or anintermittent fault caused the execution of the query to be unsuccessfulbased on whether the retry of the execution of the query was successfulor unsuccessful.

FIG. 11 is a schematic flow chart diagram of a method 1100 for retryinga query. The method 1100 may be executed by any suitable computingresources such as a compute service manager 102, a query retry module114, and/or a resource manager 802. The method 1100 may be executed byone or more database query managers which may generically refer toeither of the compute service manager 102 and/or the resource manager802.

The method 1100 begins and a computing resource receives at 1102 a querydirected to database data. A first database query manager assigns at1104 execution of the query to one or more nodes of an executionplatform. The method 1100 continues and a computing resource determinesat 1106 that execution of the query was unsuccessful. The first databasequery manager reassigns at 1108 the query to a second database querymanager. The method 1100 continues and the second database query managerassigns at 1110 a retry of the execution of the query to one or moreexecution nodes of an execution platform.

FIG. 12 is a schematic flow chart diagram of a method 1200 for retryinga query. The method 1200 may be executed by any suitable computingresources such as a compute service manager 102, a query retry module114, and/or a resource manager 802. The method 1200 may be executed byone or more database query managers which may generically refer toeither of the compute service manager 102 and/or the resource manager802.

The method 1200 begins and a computing resource receives at 1202 a querydirected to database data, wherein the query is received from a clientaccount. The method 1200 continues and a computing resource receives at1204 an indication that execution of the query was unsuccessful. Acomputing resource automatically assigns at 1206 one or more retries ofexecuting the query until execution of the query is successful. Themethod 1200 continues and a computing resource logs at 1208 anindication of each attempt to execute the query in a transaction logassociated with the client account. A computing resource receives at1210 a request for the transaction log from the client account. Acomputing resource generates at 1212 a filtered transaction log byfiltering out each unsuccessful attempt to execute the query. Acomputing resource provides at 1214 the filtered transaction log to theclient account.

FIG. 13 is a schematic flow chart diagram of a method 1300 for retryinga query. The method 1300 may be executed by any suitable computingresources such as a compute service manager 102, a query retry module114, and/or a resource manager 802. The method 1300 may be executed byone or more database query managers which may generically refer toeither of the compute service manager 102 and/or the resource manager802.

The method 1300 begins and a resource manager 802 receives at 1302 aquery directed to database data from a client account. The resourcemanager 802 assigns at 1304 an original execution of the query to one ormore execution nodes of an execution platform. The resource manager 802determines at 1306 the original execution of the query was unsuccessful.The resource manager 802 transfers at 1308 the query to a computeservice manager 102 configured to manage internal tasks for improvingoperation of a database platform that are not received from clientaccounts. The compute service manager 102 assigns at 1310 a retryexecution of the query to one or more execution nodes of an executionplatform.

FIG. 14 is a schematic flow chart diagram of a method 1400 for retryinga query. The method 1400 may be executed by any suitable computingresources such as a compute service manager 102, a query retry module114, and/or a resource manager 802. The method 1400 may be executed byone or more database query managers which may generically refer toeither of the compute service manager 102 and/or the resource manager802.

The method 1400 begins and a computing resource receives at 1402 a querydirected to database data, wherein the query is received from a clientaccount. The method 1400 continues and a computing resource receives at1404 an indication that execution of the query was unsuccessful, whereinthe execution of the query was attempted on a first version of adatabase platform. A computing resource determines at 1406 whether thefirst version of the database platform is the most recent version of thedatabase platform. A computing resource assigns at 1408, in response todetermining the first version is the most recent version, a first retryexecution of the query on the first version of the database platform. Acomputing resource assess at 1510 results of at least the first retryexecution to determine whether a regression might exist in the firstversion of the database platform.

FIG. 15 is a block diagram depicting an example computing device 1500.In some embodiments, computing device 1500 is used to implement one ormore of the systems and components discussed herein. For example,computing device 1500 may allow a user or administrator to accesscompute service manager 102 and/or resource manager 802. Further,computing device 1500 may interact with any of the systems andcomponents described herein. Accordingly, computing device 1500 may beused to perform various procedures and tasks, such as those discussedherein. Computing device 1500 can function as a server, a client or anyother computing entity. Computing device 1500 can be any of a widevariety of computing devices, such as a desktop computer, a notebookcomputer, a server computer, a handheld computer, a tablet, and thelike.

Computing device 1500 includes one or more processor(s) 1502, one ormore memory device(s) 1504, one or more interface(s) 1506, one or moremass storage device(s) 1508, and one or more Input/Output (I/O)device(s) 1510, all of which are coupled to a bus 1512. Processor(s)1502 include one or more processors or controllers that executeinstructions stored in memory device(s) 1504 and/or mass storagedevice(s) 1508. Processor(s) 1502 may also include various types ofcomputer-readable media, such as cache memory.

Memory device(s) 1504 include various computer-readable media, such asvolatile memory (e.g., random access memory (RAM)) and/or nonvolatilememory (e.g., read-only memory (ROM)). Memory device(s) 1504 may alsoinclude rewritable ROM, such as Flash memory.

Mass storage device(s) 1508 include various computer readable media,such as magnetic tapes, magnetic disks, optical disks, solid statememory (e.g., Flash memory), and so forth. Various drives may also beincluded in mass storage device(s) 1508 to enable reading from and/orwriting to the various computer readable media. Mass storage device(s)1508 include removable media and/or non-removable media.

I/O device(s) 1510 include various devices that allow data and/or otherinformation to be input to or retrieved from computing device 1500.Example I/O device(s) 1510 include cursor control devices, keyboards,keypads, microphones, monitors or other display devices, speakers,printers, network interface cards, modems, lenses, CCDs or other imagecapture devices, and the like.

Interface(s) 1506 include various interfaces that allow computing device1500 to interact with other systems, devices, or computing environments.Example interface(s) 1506 include any number of different networkinterfaces, such as interfaces to local area networks (LANs), wide areanetworks (WANs), wireless networks, and the Internet.

Bus 1512 allows processor(s) 1502, memory device(s) 1504, interface(s)1506, mass storage device(s) 1508, and I/O device(s) 1510 to communicatewith one another, as well as other devices or components coupled to bus1512. Bus 1512 represents one or more of several types of busstructures, such as a system bus, PCI bus, IEEE 1394 bus, USB bus, andso forth.

For purposes of illustration, programs and other executable programcomponents are shown herein as discrete blocks, although it isunderstood that such programs and components may reside at various timesin different storage components of computing device 1500 and areexecuted by processor(s) 1502. Alternatively, the systems and proceduresdescribed herein can be implemented in hardware, or a combination ofhardware, software, and/or firmware. For example, one or moreapplication specific integrated circuits (ASICs) can be programmed tocarry out one or more of the systems and procedures described herein.

EXAMPLES

The following examples pertain to further embodiments:

Example 1 is a method. The method includes receiving a query directed todatabase data and assigning execution of the query to one or moreexecution nodes of a database platform, wherein the one or moreexecution nodes configured to execute the query on a first version ofthe database platform. The method includes determining that execution ofthe query was unsuccessful. The method includes assigning a first retryexecution of the query on the first version of the database platform.The method includes assigning a second retry execution of the query on asecond version of the database platform.

Example 2 is a method as in Example 1, further comprising: determiningwhether the first retry execution on the first version of the databaseplatform is successful; and determining whether the second retryexecution on the second version of the database platform is successful.

Example 3 is a method as in any of Examples 1-2, further comprising, inresponse to determining the first retry execution is unsuccessful andthe second retry execution is successful, generating a report indicatingthat a regression might exist in the first version of the databaseplatform.

Example 4 is a method as in any of Examples 1-3, further comprising, inresponse to determining the first retry execution is successful and thesecond retry execution is successful, generating a report indicatingthat the original, unsuccessful execution of the query might have faileddue to an intermittent fault.

Example 5 is a method as in any of Examples 1-4, further comprising, inresponse to determining the first retry execution is unsuccessful andthe second retry execution is unsuccessful, generating a reportindicating one or more of: a regression might exist in the first versionof the database platform; a regression might exist in the second versionof the database platform; or an error might exist with at least one ofthe one or more execution nodes of the database platform that attemptedthe original, unsuccessful execution of the query.

Example 6 is a method as in any of Examples 1-5, wherein: assigning theexecution of the query to the one or more execution nodes is carried outby a resource manager that received the query directed to the databasedata; assigning the first retry execution of the query is carried out bya compute service manager configured to manage internal database tasksthat are not received from a client account; and the assigning thesecond retry execution of the query is carried out by a compute servicemanager configured to manage internal database tasks that are notreceived from a client account.

Example 7 is a method as in any of Examples 1-6, wherein assigning thefirst retry execution of the query on the first version of the databaseplatform further comprises one or more of: assigning a compute servicemanager to manage operation of the first retry execution of the query;or identifying one or more execution nodes to perform the first retryexecution of the query.

Example 8 is a method as in any of Examples 1-7, further comprising, inresponse to at least one of the first retry execution or the secondretry execution being successful, storing a response to the query suchthat the response is accessible by a client account that requested thequery.

Example 9 is a method as in any of Examples 1-8, further comprisingdetermining whether the query can be retried based on whether StructuredQuery Language (SQL) text for the query has been truncated.

Example 10 is a method as in any of Examples 1-9, further comprisingdetermining whether the execution of the query was unsuccessful due toan internal error or a user error, wherein the internal error is anerror associated with the database platform and the user error is anerror associated with the text of the query, and wherein the assigningthe first retry execution and the assigning the second retry executionoccurs only if the original, unsuccessful execution of the queryoccurred due to an integral error.

Example 11 is a system. The system includes a multiple tenantcloud-based database platform comprising a plurality of shared storagedevices collectively storing database data and an execution platformindependent from the plurality of shared storage device. The systemincludes one or more processors for managing database tasks. The one ormore processors are configured to receive a query directed to databasedata. The one or more processors are configured to assign execution ofthe query to one or more execution nodes of a database platform, the oneor more execution nodes configured to execute the query on a firstversion of the database platform. The one or more processors areconfigured to determine that execution of the query was unsuccessful.The one or more processors are configured to assign a first retryexecution of the query on the first version of the database platform.The one or more processors are configured to assign a second retryexecution of the query on a second version of the database platform.

Example 12 is a system as in Example 11, wherein the one or moreprocessors are further configured to: determine whether the first retryexecution on the first version of the database platform is successful;and determine whether the second retry execution on the second versionof the database platform is successful.

Example 13 is a system as in any of Examples 11-12, wherein the one ormore processors are further configured to, in response to determiningthe first retry execution is unsuccessful and the second retry executionis successful, generate a report indicating that a regression mightexist in the first version of the database platform.

Example 14 is a system as in any of Examples 11-13, wherein the one ormore processors are further configured to, in response to determiningthe first retry execution is successful and the second retry executionis successful, generate a report indicating that the original,unsuccessful execution of the query might have failed due to anintermittent fault.

Example 15 is a system as in any of Examples 11-14, wherein the one ormore processors are further configured to, in response to determiningthe first retry execution is unsuccessful and the second retry executionis unsuccessful, generate a report indicating one or more of: aregression might exist in the first version of the database platform; aregression might exist in the second version of the database platform;or an error might exist with at least one of the one or more executionnodes of the database platform that attempted the original, unsuccessfulexecution of the query.

Example 16 is a system as in any of Examples 11-15, wherein: the one ormore processors that assign the execution of the query to the one ormore execution nodes are part of a resource manager that received thequery directed to the database data; the one or more processors thatassign that assign the first retry execution of the query are part of acompute service manager configured to manage internal database tasksthat are not received from a client account; and the one or moreprocessors that assign that assign the second retry execution of thequery are part of a compute service manager configured to manageinternal database tasks that are not received from a client account.

Example 17 is a system as in any of Examples 11-16, wherein the one ormore processors are configured to assign the first retry execution ofthe query on the first version of the database platform by one or moreof: assigning a compute service manager to manage operation of the firstretry execution of the query; or identifying one or more execution nodesto perform the first retry execution of the query.

Example 18 is a system as in any of Examples 11-17, wherein the one ormore processors are further configured to, in response to at least oneof the first retry execution or the second retry execution beingsuccessful, store a response to the query such that the response isaccessible by a client account that requested the query.

Example 19 is a system as in any of Examples 11-18, wherein the one ormore processors are further configured to determine whether the querycan be retried based on whether Structured Query Language (SQL) text forthe query has been truncated.

Example 20 is a system as in any of Examples 11-19, wherein the one ormore processors are further configured to determine the execution of thequery was unsuccessful due to an internal error or a user error, whereinthe internal error is an error associated with the database platform andthe user error is an error associated with the text of the query, andwherein the one or more processors are configured to assign the firstretry execution and assign the second retry execution only if theoriginal, unsuccessful execution of the query occurred due to anintegral error.

Example 21 is one or more processors configurable to executeinstructions stored in non-transitory computer readable storage media.The instructions include receiving a query directed to database data.The instructions include assigning execution of the query to one or moreexecution nodes of a database platform, the one or more execution nodesconfigured to execute the query on a first version of the databaseplatform. The instructions include determining that execution of thequery was unsuccessful. The instructions include assigning a first retryexecution of the query on the first version of the database platform.The instructions include assigning a second retry execution of the queryon a second version of the database platform.

Example 22 is one or more processors as in Example 21, wherein theinstructions further comprise: determining whether the first retryexecution on the first version of the database platform is successful;and determining whether the second retry execution on the second versionof the database platform is successful.

Example 23 is one or more processors as in any of Examples 21-22,wherein the instructions further comprise, in response to determiningthe first retry execution is unsuccessful and the second retry executionis successful, generating a report indicating that a regression mightexist in the first version of the database platform.

Example 24 is one or more processors as in any of Examples 21-23,wherein the instructions further comprise, in response to determiningthe first retry execution is successful and the second retry executionis successful, generating a report indicating that the original,unsuccessful execution of the query might have failed due to anintermittent fault.

Example 25 is one or more processors as in any of Examples 21-24,wherein the instructions further comprise, in response to determiningthe first retry execution is unsuccessful and the second retry executionis unsuccessful, generating a report indicating one or more of: aregression might exist in the first version of the database platform; aregression might exist in the second version of the database platform;or an error might exist with at least one of the one or more executionnodes of the database platform that attempted the original, unsuccessfulexecution of the query.

Example 26 is one or more processors as in any of Examples 21-25,wherein the instructions are such that: assigning the execution of thequery to the one or more execution nodes is carried out by a resourcemanager that received the query directed to the database data; theassigning the first retry execution of the query is carried out by acompute service manager configured to manage internal database tasksthat are not received from a client account; and the assigning thesecond retry execution of the query is carried out by a compute servicemanager configured to manage internal database tasks that are notreceived from a client account.

Example 27 is one or more processors as in any of Examples 21-26,wherein the instructions are such that assigning the first retryexecution of the query on the first version of the database platformfurther comprises one or more of: assigning a compute service manager tomanage operation of the first retry execution of the query; oridentifying one or more execution nodes to perform the first retryexecution of the query.

Example 28 is one or more processors as in any of Examples 21-27,wherein the instructions further comprise, in response to at least oneof the first retry execution or the second retry execution beingsuccessful, storing a response to the query such that the response isaccessible by a client account that requested the query.

Example 29 is one or more processors as in any of Examples 21-28,wherein the instructions further comprise determining whether the querycan be retried based on whether Structured Query Language (SQL) text forthe query has been truncated.

Example 30 is one or more processors as in any of Examples 21-29,wherein the instructions further comprise determining whether theexecution of the query was unsuccessful due to an internal error or auser error, wherein the internal error is an error associated with thedatabase platform and the user error is an error associated with thetext of the query, and wherein the assigning the first retry executionand the assigning the second retry execution occurs only if theoriginal, unsuccessful execution of the query occurred due to anintegral error.

Example 31 is a method. The method includes receiving a query directedto database data and assigning execution of the query to one or moreexecution nodes of an execution platform, the one or more executionnodes configured to execute the query on a first version of a databaseplatform. The method includes determining that execution of the querywas unsuccessful. The method includes assigning a first retry executionof the query to the one or more execution nodes of the executionplatform. The method includes determining whether a regression or anintermittent fault caused the execution of the query to be unsuccessfulbased at least in part on whether the first retry execution of the querywas successful or unsuccessful.

Example 32 is a method as in Example 31, further comprising assigning asecond retry execution of the query to one or more other execution nodesthat did not attempt the execution of the original, unsuccessfulexecution of the query.

Example 33 is a method as in any of Examples 31-32, further comprisingassigning a third retry execution of the query to be performed on asecond version of the database platform.

Example 34 is a method as in any of Examples 31-33, wherein determiningwhether a regression or an intermittent fault caused the execution ofthe query to be unsuccessful comprises determining based on results ofthe first retry execution, the second retry execution, and the thirdretry execution of the query.

Example 35 is a method as in any of Examples 31-34, further comprising,in response to determining the first retry execution is unsuccessful andthe second retry execution is successful, generating a report indicatingthat the original, unsuccessful execution of the query might have faileddue to an issue within at least one of the one or more execution nodes.

Example 36 is a method as in any of Examples 31-35, further comprising,in response to determining the first retry execution is unsuccessful andthe third retry execution is successful, generating a report indicatingthat a regression might exist in the first version of the databaseplatform.

Example 37 is a method as in any of Examples 31-36, further comprising,in response to determining the first retry execution is unsuccessful,the second retry execution is unsuccessful, and the third retryexecution is unsuccessful, generating a report indicating one or moreof: a regression might exist in the first version of the databaseplatform; a regression might exist in the second version of the databaseplatform; an issue might exist with at least one of the one or moreexecution nodes of the database platform; an issue might exist with atleast one of the one or more other execution nodes associated with thesecond retry execution; or an intermittent fault might be occurring.

Example 38 is a method as in any of Examples 31-37, further comprisingdetermining whether the query can be retried based on whether StructuredQuery Language (SQL) text for the query has been truncated.

Example 39 is a method as in any of Examples 31-38, further comprisingdetermining whether the execution of the query was unsuccessful due toan internal error or a user error, wherein the internal error is anerror associated with the database platform and the user error is anerror associated with the text of the query, and wherein the assigningthe first retry execution and the assigning the second retry executionoccurs only if the original, unsuccessful execution of the queryoccurred due to an integral error.

Example 40 is a method as in any of Examples 31-39, further comprisinggenerating a transaction log comprising an entry for each attempt toexecute the query.

Example 41 is a method. The method includes receiving a query directedto database data and assigning, by a first database query manager,execution of the query to one or more execution nodes of an executionplatform. The method includes determining that execution of the querywas unsuccessful. The method includes reassigning, by the first databasequery manager, the query to a second database query manager to beretried. The method includes assigning, by the second database querymanager, a retry of the execution of the query to one or more executionnodes of an execution platform.

Example 42 is a method as in Example 41, further comprising determiningwhether the query was originally executed on a new version of thedatabase platform, and wherein reassigning the query to the seconddatabase query manager is done in response to determining the query wasoriginally executed on a new version of the database platform.

Example 43 is a method as in any of Examples 41-42, further comprisingdetermining whether a regression might exist in the new version of thedatabase platform based at least in part on the results of the retry ofthe execution of the query.

Example 44 is a method as in any of Examples 41-43, wherein thereassigning the query to the second database query manager is performedin response to determining that execution of the query was unsuccessfuldue to an internal error.

Example 45 is a method as in any of Examples 41-44, further comprising:determining whether the execution of the query was unsuccessful due toan internal error; in response to determining the execution of the querywas unsuccessful due to an internal error, generating an error messagefor an account that requested the query indicating the query failed dueto an internal error; and recording a service incident indicating thequery failed due to an internal error.

Example 46 is a method as in any of Examples 41-45, wherein thereassigning the query to the second database query manager is performedin response to the recording the service incident indicating the queryfailed due to an internal error.

Example 47 is a method as in any of Examples 41-46, wherein the firstdatabase query manager is running a first version of a database platformand the second database query manager is running a second version of thedatabase platform, wherein the first version and the second version ofthe database platform were released at different times and comprisedifferent software.

Example 48 is a method as in any of Examples 41-47, wherein the databaseplatform comprises multiple database query managers collectively runningtwo or more versions of the database platform at one time.

Example 49 is a method as in any of Examples 41-48, further comprisinganalyzing results of the retry of the execution of the query todetermine whether the execution of the query was unsuccessful likely dueto an intermittent fault or a regression in the first version of thedatabase platform.

Example 50 is a method as in any of Examples 41-49, wherein assigningthe retry of the execution of the query comprises assigning execution ofthe query to different execution nodes that did not attempt theexecution of the query.

Example 51 is a method. The method includes receiving a query directedto database data from a client account and receiving an indication thatan original execution of the query was unsuccessful. The method includesautomatically assigning retrying execution of the query until executionof the query is successful. The method includes logging an indication ofeach attempt to execute the query in a transaction log associated withthe client account. The method includes receiving a request for thetransaction log from the client account and generating a filteredtransaction log by filtering out each unsuccessful attempt to executethe query. The method includes providing the filtered transaction log tothe client account.

Example 52 is a method as in Example 51, wherein automatically assigningretrying execution of the query comprises: assigning a query retry to bemanaged by a first database query manager that managed the originalexecution of the query; and assigning a query retry to be managed by asecond database query manager that did not manage the original executionof the query.

Example 53 is a method as in any of Examples 51-52, further comprisinganalyzing all query retry attempts to determine whether the originalexecution of the query was unsuccessful due to an intermittent fault ora regression in software run by the first database query manager.

Example 54 is a method as in any of Examples 51-53, whereinautomatically assigning retrying execution of the query comprises:assigning a query retry to be performed by a first set of executionnodes that attempted the original execution of the query; and assigninga query retry to be performed by a second set of execution nodes thatdid not attempt the original execution of the query.

Example 55 is a method as in any of Examples 51-54, further comprisinganalyzing all query retry attempts to determine whether the originalexecution of the query was unsuccessful due to an intermittent fault ora hardware issue on at least one execution node of the first set ofexecution nodes.

Example 56 is a method as in any of Examples 51-55, whereinautomatically assigning retrying execution of the query comprises:assigning a query retry to be performed on a first version of a databaseplatform that was used to attempt the original execution of the query;and assigning a query retry to be performed on a second version of thedatabase platform that was not used to attempt the original execution ofthe query.

Example 57 is a method as in any of Examples 51-56, further comprisinganalyzing all query retry attempts to determine whether the originalexecution of the query was unsuccessful due to an intermittent fault ora regression in the first version of the database platform.

Example 58 is a method as in any of Examples 51-57, whereinautomatically assigning retrying the execution of the query comprisesassigning each query retry attempt to a single instance of a databasequery manager such that no query retry attempt is managed by more thanone instance of a database query manager.

Example 59 is a method as in any of Examples 51-58, wherein the loggingthe indication of each attempt to execute the query comprises loggingone or more of: an indication of which instance of a database querymanager managed each attempt; an indication of which version of adatabase platform was run to perform each attempt; an indication ofwhich execution nodes were used to perform each attempt; an indicationof when each attempt was started; or an indication of when each attemptwas completed.

Example 60 is a method as in any of Examples 51-59, further comprising:in response to receiving the indication that the original execution ofthe query was unsuccessful, providing a notification to the clientaccount indicating that the original execution of the query wasunsuccessful; in response to receiving an indication that at least oneretry attempt is successful, providing a notification to the clientaccount indicating that the query has been successfully executed.

Example 61 is a method. The method includes receiving a query directedto database data from a client account. The method includes receiving anindication that an original execution of the query was unsuccessful,wherein the original execution of the query was attempted on a firstversion of a database platform. The method includes determining whetherthe first version of the database platform is a most recent version ofthe database platform. The method includes, in response to determiningthe first version is the most recent version, assigning a first retryexecution of the query on the first version of the database platform.The method includes assessing results of at least the first retryexecution to determine whether a regression might exist in the firstversion of the database platform.

Example 62 is a method as in Example 61, further comprising assigning asecond retry execution of the query on a second version of the databaseplatform, wherein the second version of the database platform is not themost recent version of the database platform.

Example 63 is a method as in any of Examples 61-62, further comprisingassessing results of the first retry execution and the second retryexecution to determine whether a regression might exist in the firstversion of the database platform.

Example 64 is a method as in any of Examples 61-63, further comprisingdetermining whether Structured Query Language (SQL) text for the queryhas been truncated, and wherein assigning the first retry execution ofthe query is performed only if the SQL text for the query has not beentruncated.

Example 65 is a method as in any of Examples 61-64, further comprisingpopulating a transaction log for the client account comprising a listingof all actions performed for the client account, wherein populating thetransaction log comprising entering an indication that the originalexecution of the query was unsuccessful.

Example 66 is a method as in any of Examples 61-65, further comprisingdetermining whether the original execution of the query was unsuccessfuldue to an internal error, and wherein the assigning the first retryexecution of the query is performed only if the original execution ofthe query is unsuccessful due to an internal error.

Example 67 is a method as in any of Examples 61-66, further comprisingstoring a record of the original execution of the query in a key valuestore, wherein the record comprises one or more of: Structured QueryLanguage (SQL) text for the query; a start timestamp when execution ofthe query began; a completion timestamp when execution of the queryfailed; an indication of whether the query failed due to an internalerror, an error in the SQL text for the query; or an intermittent fault;or a unique identification for the query that enables the client accountto access results of the query.

Example 68 is a method as in any of Examples 61-67, wherein the query isassociated with a unique identification to enable the client account toaccess the query and wherein the method further comprises: receiving anindication that at least one retry attempt for the query is successful;and rerouting the unique identification to point to a successful retryattempt rather than the original execution of the query.

Example 69 is a method as in any of Examples 61-68, wherein theassigning the first retry execution of the query comprises: removing thequery from a resource manager configured to manage external tasksreceived from the client account; and assigning the first retryexecution to a compute service manager configured to manage internaltasks for improving operation of the database platform that are notreceived from the client account.

Example 70 is a method as in any of Examples 61-69, wherein assigningthe first retry execution to the compute service manager comprisesentering one or more retry attempts in a queue of the compute service,wherein the queue of the compute service manager comprises a listing ofall internal tasks for improving operation of the database platform.

Example 71 is a method. The method includes receiving, by a firstdatabase query manager, a query directed to database data from a clientaccount. The method includes assigning an original execution of thequery to one or more execution nodes of an execution platform. Themethod includes determining the original execution of the query wasunsuccessful. The method includes transferring the query to a seconddatabase query manager configured to manage internal tasks for improvingoperation of a database platform that are not received from clientaccounts. The method includes assigning, by the second database querymanager, a retry execution of the query to one or more execution nodesof an execution platform.

Example 72 is a method as in Example 71, wherein the first databasequery manager is configured to manage external tasks received fromclient accounts.

Example 73 is a method as in any of Examples 71-72, further comprisingidentifying the second database query manager based on one or more of:whether the second database query manager is implementing the sameversion of the database platform as the first database query manager; aworkload of the second database query manager; or whether the version ofthe database platform implemented by the first database query managerand/or the second database query manager is a most recent version of thedatabase platform.

Example 74 is a method as in any of Examples 71-73, further comprising,in response to determining the first database query manager and thesecond database query manager are implementing the same version of thedatabase platform, transferring the query to the second database querymanager and further assigning the query to a third database querymanager that is configured to manage internal tasks and is implementinga different version of the database platform.

Example 75 is a method as in any of Examples 71-74, wherein thetransferring the query to the second database query manager comprisingentering the query as a job in a queue of the second database querymanager, wherein the queue receives a plurality of jobs for improvingthe operation of the database platform.

Example 76 is a method as in any of Examples 71-75, wherein the one ormore execution nodes of the execution platform that attempted theoriginal execution of the query are each running the same version of thedatabase platform, wherein the database platform comprises a pluralityof execution nodes collectively running multiple versions of thedatabase platform.

Example 77 is a method as in any of Examples 71-76, further comprisingdetermining whether the retry execution of the query should be assignedto the one or more execution nodes of the execution platform thatattempted the original execution of the query based on one or more of:whether the one or more execution nodes are running the most recentversion of the database platform; whether an issue has been identifiedin a server of at least one of the one or more execution nodes; whetherthe one or more execution nodes have at least a portion of dataresponsive to the query stored in cache storage; a storage availabilityof the one or more execution nodes; or a processing availability of theone or more execution nodes.

Example 78 is a method as in any of Examples 71-77, wherein the databaseplatform comprises a plurality of database query managers collectivelyimplementing two or more versions of the database platform, wherein newversions of the database platform are implemented on a portion of theplurality of database query managers.

Example 79 is a method as in any of Examples 71-78, wherein the clientaccount is a tenant in a multiple tenant cloud-based database platformand the method further comprises: tracking an amount of processingresources used to execute the original execution of the query and theretry execution of the query; associating the tracked processingresources with the client account; and providing a log to the clientaccount of all processing resources used by the client account.

Example 80 is a method as in any of Examples 71-79, further comprisingdetermining whether the original execution of the query failed due to anerror in Structured Query Language (SQL) text of the query or aninternal error, wherein the method comprises transferring the query tothe second database query manager only if the original execution of thequery failed due to an internal error.

Example 81 is means for implementing any of the methods in Examples1-80.

Example 82 is non-transitory computer readable storage media storinginstructions for implementing any of the methods in Examples 1-80.

Example 83 is a multiple tenant cloud-based database platform comprisingprocessors configurable to execute instructions stored in non-transitorycomputer readable storage media, wherein the instructions comprise anyof the methods in Examples 1-80.

Example 84 is one or more processors configurable to executioninstructions, wherein the instructions comprise any of the methods inExamples 1-80.

Many of the functional units described in this specification may beimplemented as one or more components, which is a term used to moreparticularly emphasize their implementation independence. For example, acomponent may be implemented as a hardware circuit comprising customvery large-scale integration (VLSI) circuits or gate arrays,off-the-shelf semiconductors such as logic chips, transistors, or otherdiscrete components. A component may also be implemented in programmablehardware devices such as field programmable gate arrays, programmablearray logic, programmable logic devices, or the like.

Components may also be implemented in software for execution by varioustypes of processors. An identified component of executable code may, forinstance, comprise one or more physical or logical blocks of computerinstructions, which may, for instance, be organized as an object, aprocedure, or a function. Nevertheless, the executables of an identifiedcomponent need not be physically located together but may comprisedisparate instructions stored in different locations which, when joinedlogically together, comprise the component and achieve the statedpurpose for the component.

Indeed, a component of executable code may be a single instruction, ormany instructions, and may even be distributed over several differentcode segments, among different programs, and across several memorydevices. Similarly, operational data may be identified and illustratedherein within components and may be embodied in any suitable form andorganized within any suitable type of data structure. The operationaldata may be collected as a single data set or may be distributed overdifferent locations including over different storage devices, and mayexist, at least partially, merely as electronic signals on a system ornetwork. The components may be passive or active, including agentsoperable to perform desired functions.

Reference throughout this specification to “an example” means that afeature, structure, or characteristic described in connection with theexample is included in at least one embodiment of the presentdisclosure. Thus, the appearances of the phrase “in an example” invarious places throughout this specification are not necessarily allreferring to the same embodiment.

As used herein, a plurality of items, structural elements, compositionalelements, and/or materials may be presented in a common list forconvenience. However, these lists should be construed as though eachmember of the list is individually identified as a separate and uniquemember. Thus, no individual member of such list should be construed as ade facto equivalent of any other member of the same list solely based onits presentation in a common group without indications to the contrary.In addition, various embodiments and examples of the present disclosuremay be referred to herein along with alternatives for the variouscomponents thereof. It is understood that such embodiments, examples,and alternatives are not to be construed as de facto equivalents of oneanother but are to be considered as separate and autonomousrepresentations of the present disclosure.

Although the foregoing has been described in some detail for purposes ofclarity, it will be apparent that certain changes and modifications maybe made without departing from the principles thereof. It should benoted that there are many alternative ways of implementing both theprocesses and apparatuses described herein. Accordingly, the presentembodiments are to be considered illustrative and not restrictive.

Those having skill in the art will appreciate that many changes may bemade to the details of the above-described embodiments without departingfrom the underlying principles of the disclosure. The scope of thepresent disclosure should, therefore, be determined only by thefollowing claims.

Further, it should be noted, and particularly with reference to theclaims below, a “first retry attempt,” a “second retry attempt,” a“third retry attempt,” and so forth are not necessarily performed insequential order unless specifically indicated. The indicators of“first,” “second,” “third,” and so forth are included for simplifyingreference only and are not limiting to the scope of the claims. Theparameters of different retry attempts may be performed in any order andare not limited by the “first,” “second,” and “third” indicators.

What is claimed is:
 1. A method comprising: receiving, by a firstdatabase query manager, a query directed to database data from a clientaccount; assigning an original execution of the query to one or moreexecution nodes of an execution platform; determining the originalexecution of the query was unsuccessful; transferring the query to asecond database query manager configured to manage internal tasks forimproving operation of a database platform that are not received fromclient accounts; assigning, by the second database query manager, aretry execution of the query to one or more execution nodes of anexecution platform.
 2. The method of claim 1, wherein the first databasequery manager is configured to manage external tasks received fromclient accounts.
 3. The method of claim 1, further comprisingidentifying the second database query manager based on one or more of:whether the second database query manager is implementing the sameversion of the database platform as the first database query manager; aworkload of the second database query manager; or whether the version ofthe database platform implemented by the first database query managerand/or the second database query manager is a most recent version of thedatabase platform.
 4. The method of claim 3, further comprising, inresponse to determining the first database query manager and the seconddatabase query manager are implementing the same version of the databaseplatform, transferring the query to the second database query managerand further assigning the query to a third database query manager thatis configured to manage internal tasks and is implementing a differentversion of the database platform.
 5. The method of claim 1, wherein thetransferring the query to the second database query manager comprisingentering the query as a job in a queue of the second database querymanager, wherein the queue receives a plurality of jobs for improvingthe operation of the database platform.
 6. The method of claim 1,wherein the one or more execution nodes of the execution platform thatattempted the original execution of the query are each running the sameversion of the database platform, wherein the database platformcomprises a plurality of execution nodes collectively running multipleversions of the database platform.
 7. The method of claim 1, furthercomprising determining whether the retry execution of the query shouldbe assigned to the one or more execution nodes of the execution platformthat attempted the original execution of the query based on one or moreof: whether the one or more execution nodes are running the most recentversion of the database platform; whether an issue has been identifiedin a server of at least one of the one or more execution nodes; whetherthe one or more execution nodes have at least a portion of dataresponsive to the query stored in cache storage; a storage availabilityof the one or more execution nodes; or a processing availability of theone or more execution nodes.
 8. The method of claim 1, wherein thedatabase platform comprises a plurality of database query managerscollectively implementing two or more versions of the database platform,wherein new versions of the database platform are implemented on aportion of the plurality of database query managers.
 9. The method ofclaim 1, wherein the client account is a tenant in a multiple tenantcloud-based database platform and the method further comprises: trackingan amount of processing resources used to execute the original executionof the query and the retry execution of the query; associating thetracked processing resources with the client account; and providing alog to the client account of all processing resources used by the clientaccount.
 10. The method of claim 1, further comprising determiningwhether the original execution of the query failed due to an error inStructured Query Language (SQL) text of the query or an internal error,wherein the method comprises transferring the query to the seconddatabase query manager only if the original execution of the queryfailed due to an internal error.
 11. A system comprising: a multipletenant cloud-based database platform comprising a plurality of sharedstorage devices collectively storing database data and an executionplatform independent of the plurality of shared storage devices; a firstdatabase query manager configured to manage external tasks received fromclient accounts, the first database query manager configured to: receivea query from a client account, the query directed to database datastored across one or more of the plurality of shared storage devices;reference metadata to identify one or more files stored across theplurality of shared storage devices that are responsive to the query;assign processing of the one or more files to one or more executionnodes of an execution platform as an original execution of the query;determine the original execution of the query was unsuccessful; andtransfer the query to a second database query manager configured tomanage internal tasks for improving operation of the multiple tenantcloud-based database platform, wherein the internal tasks are notreceived from client accounts; and the second database query managerconfigured to assign processing of the one or more files to one or moreexecution nodes of an execution platform as a retry execution of thequery.
 12. The system of claim 11, wherein the multiple tenantcloud-based database platform comprises a plurality of database querymanagers for managing external tasks and internal tasks, wherein theplurality of database query managers collectively implement multipleversions of software for the multiple tenant cloud-based databaseplatform.
 13. The system of claim 11, wherein the first database querymanager is further configured to identify the second database querymanager based on one or more of: whether the second database querymanager is implementing the same version of the database platform as thefirst database query manager; a workload of the second database querymanager; or whether the version of the database platform implemented bythe first database query manager and/or the second database querymanager is a most recent version of the database platform.
 14. Thesystem of claim 13, wherein the first database query manager is furtherconfigured to, in response to determining the first database querymanager and the second database query manager are implementing the sameversion of the database platform, transferring the query to the seconddatabase query manager and further assigning the query to a thirddatabase query manager that is configured to manage internal tasks andis implementing a different version of the database platform.
 15. Thesystem of claim 11, wherein the first database query manager isconfigured to transfer the query to the second database query manager byentering the query as a job in a queue of the second database querymanager, wherein the queue receives a plurality of jobs for improvingthe operation of the database platform.
 16. The system of claim 11,wherein the one or more execution nodes of the execution platform thatattempted the original execution of the query are each running the sameversion of the database platform, wherein the database platformcomprises a plurality of execution nodes collectively running multipleversions of the database platform.
 17. The system of claim 11, whereinthe second database query manager is configured to determine whether theretry execution of the query should be assigned to the one or moreexecution nodes of the execution platform that attempted the originalexecution of the query based on one or more of: whether the one or moreexecution nodes are running the most recent version of the databaseplatform; whether an issue has been identified in a server of at leastone of the one or more execution nodes; whether the one or moreexecution nodes have at least a portion of data responsive to the querystored in cache storage; a storage availability of the one or moreexecution nodes; or a processing availability of the one or moreexecution nodes.
 18. The system of claim 11, wherein the first databasequery manager and the second database query manager can each assigntasks to any execution nodes of the multiple tenant cloud-based databaseplatform, and wherein all execution nodes can access data stored acrossthe plurality of shared storage devices.
 19. The system of claim 11,wherein the first database query manager is further configured to trackan amount of processing resources used to execute the original executionof the query and the second database query manager is further configuredto track an amount of processing resources used to execute the retryexecution of the query.
 20. The system of claim 11, wherein the firstdatabase query manager is further configured to determine whether theoriginal execution of the query failed due to an error in StructuredQuery Language (SQL) text of the query or an internal error, wherein thefirst database query manager is configured to transfer the query to thesecond database query manager only if the original execution of thequery failed due to an internal error.
 21. One or more processorsconfigurable to execute instructions stored in non-transitory computerreadable storage media, the instructions comprising: receiving, by afirst database query manager, a query directed to database data from aclient account; assigning an original execution of the query to one ormore execution nodes of an execution platform; determining the originalexecution of the query was unsuccessful; transferring the query to asecond database query manager configured to manage internal tasks forimproving operation of a database platform that are not received fromclient accounts; assigning, by the second database query manager, aretry execution of the query to one or more execution nodes of anexecution platform.
 22. The one or more processors of claim 21, whereinthe first database query manager is configured to manage external tasksreceived from client accounts.
 23. The one or more processors of claim21, wherein the instructions further comprise identifying the seconddatabase query manager based on one or more of: whether the seconddatabase query manager is implementing the same version of the databaseplatform as the first database query manager; a workload of the seconddatabase query manager; or whether the version of the database platformimplemented by the first database query manager and/or the seconddatabase query manager is a most recent version of the databaseplatform.
 24. The one or more processors of claim 23, wherein theinstructions further comprise, in response to determining the firstdatabase query manager and the second database query manager areimplementing the same version of the database platform, transferring thequery to the second database query manager and further assigning thequery to a third database query manager that is configured to manageinternal tasks and is implementing a different version of the databaseplatform.
 25. The one or more processors of claim 21, wherein theinstructions cause the one or more processors to transfer the query tothe second database query manager by entering the query as a job in aqueue of the second database query manager, wherein the queue receives aplurality of jobs for improving the operation of the database platform.26. The one or more processors of claim 21, wherein the one or moreexecution nodes of the execution platform that attempted the originalexecution of the query are each running the same version of the databaseplatform, wherein the database platform comprises a plurality ofexecution nodes collectively running multiple versions of the databaseplatform.
 27. The one or more processors of claim 21, wherein theinstructions further comprise determining whether the retry execution ofthe query should be assigned to the one or more execution nodes of theexecution platform that attempted the original execution of the querybased on one or more of: whether the one or more execution nodes arerunning the most recent version of the database platform; whether anissue has been identified in a server of at least one of the one or moreexecution nodes; whether the one or more execution nodes have at least aportion of data responsive to the query stored in cache storage; astorage availability of the one or more execution nodes; or a processingavailability of the one or more execution nodes.
 28. The one or moreprocessors of claim 21, wherein the database platform comprises aplurality of database query managers collectively implementing two ormore versions of the database platform, wherein new versions of thedatabase platform are implemented on a portion of the plurality ofdatabase query managers.
 29. The one or more processors of claim 21,wherein the client account is a tenant in a multiple tenant cloud-baseddatabase platform and the instructions further comprises: tracking anamount of processing resources used to execute the original execution ofthe query and the retry execution of the query; associating the trackedprocessing resources with the client account; and providing a log to theclient account of all processing resources used by the client account.30. The one or more processors of claim 21, wherein the instructionsfurther comprise determining whether the original execution of the queryfailed due to an error in Structured Query Language (SQL) text of thequery or an internal error, wherein the method comprises transferringthe query to the second database query manager only if the originalexecution of the query failed due to an internal error.